{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,os\n",
    "import csv\n",
    "import pickle\n",
    "import scipy\n",
    "import numpy as np\n",
    "import json\n",
    "from IPython.display import clear_output\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproducing Main Text Results\n",
    "\n",
    "This nb is devoted to reproducing the results shown in the main text (Fig. 1,2,3,4,5). The different sections of this nb correspond to one figure in the main text.\n",
    "Be sure to have run all the previous nb and that all the scores for the different nations are in the __scores__ folder.\n",
    "\n",
    "\n",
    "# To add shap values\n",
    "\n",
    "- https://towardsdatascience.com/explain-your-model-with-the-shap-values-bc36aac4de3d\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Training Results\n",
    "\n",
    "We first create a DataFrame from the .json file in which the 01_train_model.ipynb notebook and the 01_train_all_models.py script save the summary of model trainings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_training_res = pd.read_json (\"./training_results.json\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading Scores Data\n",
    "\n",
    "We read the different score datasets and put them into two pandas dataframe. \"df_global_scores\" contains all the scores for each party, computed not dividing the data in different years; \"df_scores_in_time\" contains the scores for each party in the corresponding election year. The following shown tables are printed in html the \"main_text_figures\" folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>party</th>\n",
       "      <th>score</th>\n",
       "      <th>orientation</th>\n",
       "      <th>counts</th>\n",
       "      <th>nation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>In Common We Can</td>\n",
       "      <td>0.944162</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>1576</td>\n",
       "      <td>ES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casapound</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>Right-wing</td>\n",
       "      <td>38</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5S</td>\n",
       "      <td>0.833948</td>\n",
       "      <td>Other</td>\n",
       "      <td>271</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PaP</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>9</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>We can</td>\n",
       "      <td>0.617397</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>1644</td>\n",
       "      <td>ES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Party of Freedom</td>\n",
       "      <td>0.582543</td>\n",
       "      <td>Right-wing</td>\n",
       "      <td>527</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indomitable France</td>\n",
       "      <td>0.572755</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>323</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brothers of Italy</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>Right-wing</td>\n",
       "      <td>9</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Forward Italy</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Right-wing</td>\n",
       "      <td>15</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>National Front</td>\n",
       "      <td>0.515284</td>\n",
       "      <td>Right-wing</td>\n",
       "      <td>458</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                party     score orientation  counts nation\n",
       "0    In Common We Can  0.944162   Left-wing    1576     ES\n",
       "1           Casapound  0.894737  Right-wing      38     IT\n",
       "2                 M5S  0.833948       Other     271     IT\n",
       "3                 PaP  0.777778   Left-wing       9     IT\n",
       "4              We can  0.617397   Left-wing    1644     ES\n",
       "5    Party of Freedom  0.582543  Right-wing     527     NL\n",
       "6  Indomitable France  0.572755   Left-wing     323     FR\n",
       "7   Brothers of Italy  0.555556  Right-wing       9     IT\n",
       "8       Forward Italy  0.533333  Right-wing      15     IT\n",
       "9      National Front  0.515284  Right-wing     458     FR"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nations = [ \"IT\",\"FR\", \"ES\", \"AT\", \"DE\", \"NL\"]\n",
    "\n",
    "df_global_scores = None\n",
    "\n",
    "for nation in nations:\n",
    "    \n",
    "    if df_global_scores is None:\n",
    "        df_global_scores = pd.read_csv(\"./scores/global_scores_{}.csv\".format(nation))\n",
    "        df_global_scores[\"nation\"] = nation\n",
    "    else:\n",
    "        df_global_scores_aux = pd.read_csv(\"./scores/global_scores_{}.csv\".format(nation))\n",
    "        df_global_scores_aux[\"nation\"] = nation\n",
    "        df_global_scores = pd.concat([df_global_scores,df_global_scores_aux], axis=0)\n",
    "        \n",
    "df_global_scores.index = range(len(df_global_scores))\n",
    "df_global_scores = df_global_scores.iloc[df_global_scores.score.sort_values(ascending=False).index]\n",
    "df_global_scores.index = range(len(df_global_scores))\n",
    "df_global_scores.to_html(\"./main_text_figures/global_scores.html\")\n",
    "df_global_scores.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>party</th>\n",
       "      <th>year</th>\n",
       "      <th>score</th>\n",
       "      <th>orientation</th>\n",
       "      <th>counts</th>\n",
       "      <th>nation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Democratic Center</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>0.179688</td>\n",
       "      <td>Other</td>\n",
       "      <td>128</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Communist Refoundation Party</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>641</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVP</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>252</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>European Democracy</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>0.018868</td>\n",
       "      <td>Other</td>\n",
       "      <td>53</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Civic Choice</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>144</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Union</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>0.231707</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>82</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>UDC</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>0.024194</td>\n",
       "      <td>Other</td>\n",
       "      <td>124</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Northern League</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0.420290</td>\n",
       "      <td>Right-wing</td>\n",
       "      <td>69</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Civil Revolution</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>0.014815</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>135</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PD</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>0.330508</td>\n",
       "      <td>Left-wing</td>\n",
       "      <td>118</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          party    year     score orientation  counts nation\n",
       "0             Democratic Center  2013.0  0.179688       Other     128     IT\n",
       "1  Communist Refoundation Party  2001.0  0.007800   Left-wing     641     IT\n",
       "2                           SVP  2013.0  0.007937   Left-wing     252     IT\n",
       "3            European Democracy  2001.0  0.018868       Other      53     IT\n",
       "4                  Civic Choice  2013.0  0.055556   Left-wing     144     IT\n",
       "5                     The Union  2006.0  0.231707   Left-wing      82     IT\n",
       "6                           UDC  2013.0  0.024194       Other     124     IT\n",
       "7               Northern League  2008.0  0.420290  Right-wing      69     IT\n",
       "8              Civil Revolution  2013.0  0.014815   Left-wing     135     IT\n",
       "9                            PD  2018.0  0.330508   Left-wing     118     IT"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nations = [ \"IT\",\"FR\", \"ES\", \"AT\", \"DE\", \"NL\"]\n",
    "\n",
    "df_scores_in_time = None\n",
    "\n",
    "for nation in nations:\n",
    "    \n",
    "    if df_global_scores is None:\n",
    "        df_scores_in_time = pd.read_csv(\"./scores/scores_in_time_{}.csv\".format(nation))\n",
    "        df_scores_in_time[\"nation\"] = nation\n",
    "    else:\n",
    "        df_scores_in_time_aux = pd.read_csv(\"./scores/scores_in_time_{}.csv\".format(nation))\n",
    "        df_scores_in_time_aux[\"nation\"] = nation\n",
    "        df_scores_in_time = pd.concat([df_scores_in_time,df_scores_in_time_aux], axis=0)\n",
    "        \n",
    "df_global_scores.to_html(\"./main_text_figures/scores_in_time.html\")\n",
    "df_scores_in_time.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table 1\n",
    "\n",
    "This table summarizes the results coming from the training of the Random Forest Classifiers, used to discriminate populist manifesto sentences in different countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>AUC_valid</th>\n",
       "      <th>AUC_valid_err</th>\n",
       "      <th>F1_valid</th>\n",
       "      <th>F1_valid_err</th>\n",
       "      <th>AUC_test</th>\n",
       "      <th>F1_test</th>\n",
       "      <th>N_sentences</th>\n",
       "      <th>frac_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AT</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.15</td>\n",
       "      <td>14156</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FR</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.58</td>\n",
       "      <td>12599</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DE</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.51</td>\n",
       "      <td>30399</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.69</td>\n",
       "      <td>13004</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NL</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.50</td>\n",
       "      <td>77504</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ES</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.80</td>\n",
       "      <td>95997</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  AUC_valid  AUC_valid_err  F1_valid  F1_valid_err  AUC_test  \\\n",
       "0      AT       0.85            0.1      0.28          0.01      0.84   \n",
       "1      FR       0.79            0.1      0.39          0.01      0.80   \n",
       "2      DE       0.86            0.1      0.17          0.01      0.86   \n",
       "3      IT       0.89            0.1      0.32          0.01      0.89   \n",
       "4      NL       0.80            0.1      0.26          0.01      0.81   \n",
       "5      ES       0.93            0.1      0.75          0.01      0.95   \n",
       "\n",
       "   F1_test  N_sentences  frac_sentences  \n",
       "0     0.15        14156            0.24  \n",
       "1     0.58        12599            0.27  \n",
       "2     0.51        30399            0.17  \n",
       "3     0.69        13004            0.20  \n",
       "4     0.50        77504            0.16  \n",
       "5     0.80        95997            0.20  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_nations = [\"AT\", \"FR\", \"DE\", \"IT\",\"NL\",\"ES\"]\n",
    "models = [\"RandomForest\"]\n",
    "target_scores = [\"AUC\",\"F1\"]\n",
    "df_selected_trainings = df_training_res[(df_training_res.nation.isin(selected_nations))\\\n",
    "                                        & (df_training_res.model_type.isin(models))\\\n",
    "                                        &(df_training_res.target_score.isin(target_scores))].copy()\n",
    "df_selected_trainings = df_selected_trainings[[\"nation\",'AUC_valid',\"AUC_valid_err\",'F1_valid',\"F1_valid_err\",\"AUC_test\",\"F1_test\",\"N_sentences\",\"frac_sentences\"]]\n",
    "sort_indexes = [df_selected_trainings.loc[df_selected_trainings.nation==nation].index[0] for nation in selected_nations]\n",
    "df_selected_trainings = df_selected_trainings.loc[sort_indexes]\n",
    "df_selected_trainings.AUC_valid = df_selected_trainings.AUC_valid.apply(lambda v: round(v,2))\n",
    "df_selected_trainings.AUC_valid_err = df_selected_trainings.AUC_valid_err.apply(lambda v: max(round(v,2),0.1))\n",
    "df_selected_trainings.F1_valid = df_selected_trainings.F1_valid.apply(lambda v: round(v,2))\n",
    "df_selected_trainings.F1_valid_err = df_selected_trainings.F1_valid_err.apply(lambda v: max(round(v,2),0.01))\n",
    "df_selected_trainings.AUC_test = df_selected_trainings.AUC_test.apply(lambda v: round(v,2))\n",
    "df_selected_trainings.F1_test = df_selected_trainings.F1_test.apply(lambda v: round(v,2))\n",
    "\n",
    "\n",
    "df_selected_trainings.index = range(len(df_selected_trainings))\n",
    "df_selected_trainings = df_selected_trainings.rename(columns={\n",
    "    \"nation\":\"country\"})\n",
    "\n",
    "df_selected_trainings.to_html(\"./main_text_figures/table1.html\")\n",
    "\n",
    "df_selected_trainings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 1\n",
    "\n",
    "This figure shows the first 20 parties ranked in decreasing order by the Pop. Score. We change the party labelling for readability's sake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "party_labels = {\n",
    "    \n",
    "    \"DE\":{\n",
    "        \"Free Democratic Party\":\"FDP\",\n",
    "        \"The Left\":\"Die Linke\",\n",
    "        \"Alternative for Germany\":\"AfD\",\n",
    "        \"Christian Democratic Union-Christian Social Union\":\"CDU-CSU\"\n",
    "    },\n",
    "    \n",
    "    \"IT\":{\n",
    "        \"PD\":\"PD\",\n",
    "        \"Forward Italy\":\"FI\",\n",
    "        \"M5S\":\"M5S\",\n",
    "        \"UDC\":\"UDC\"\n",
    "    },\n",
    "    \n",
    "    \"FR\":{\n",
    "        \"Union for a Popular Movement\":\"DLR\",\n",
    "        \"National Front\":\"FN\",\n",
    "        \"Republic Onwards!\":\"LaREM\",\n",
    "        \"Socialist Party\":\"PS\"\n",
    "    },\n",
    "    \n",
    "    \"ES\":{\n",
    "        \"People's Party\":\"PP\",\n",
    "        \"We can\":\"Podemos\",\n",
    "    },\n",
    "    \n",
    "    \"NL\":{\n",
    "        \"Party of Freedom\":\"PVV\",\n",
    "        \"Forum for Democracy\":\"FvD\",\n",
    "        \"Socialist Party\":\"SP\",\n",
    "        \"Green Left\":\"GL\",        \n",
    "        \"Christian Democratic Appeal\":\"CDA\",\n",
    "    },\n",
    "    \n",
    "    \"AT\":{\n",
    "        \"Austrian Freedom Party\":\"FPO\",\n",
    "        \"Austrian People’s Party\":\"OVP\",\n",
    "    }\n",
    "    \n",
    "}\n",
    "\n",
    "df_score_sub = df_global_scores.copy()\n",
    "df_score_sub[\"label\"] = [None for party in df_score_sub.party]\n",
    "\n",
    "for nation in party_labels:\n",
    "    lab_party = []\n",
    "    for party in df_score_sub.loc[df_score_sub.nation==nation, \"party\"].values:\n",
    "        try: \n",
    "            lab_party.append(party_labels[nation][party])\n",
    "        except KeyError:\n",
    "            lab_party.append( \"missing\")\n",
    "        \n",
    "\n",
    "    df_score_sub.loc[df_score_sub.nation==nation, \"label\"] = lab_party \n",
    "\n",
    "df_score_sub = df_score_sub[df_score_sub.label != \"missing\"].copy()\n",
    "df_score_sub.index = range(len(df_score_sub))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"notebook\", font_scale=2)\n",
    "sns.set_style(\"whitegrid\")\n",
    "fig = plt.figure(figsize=(20,7))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "\n",
    "x = df_score_sub.index.values\n",
    "y = df_score_sub.score.values\n",
    "labels = [\"{0} ({1})\".format(label, nation) for label, nation in df_score_sub[[\"label\", \"nation\"]].values]\n",
    "\n",
    "ax.bar(x,y)\n",
    "\n",
    "ttt = ax.set_xticks(x)\n",
    "ttt = ax.set_xticklabels(labels, rotation=90)\n",
    "ax.set_xlim(-0.5,20.5)\n",
    "ax.set_ylabel(\"(Pop) Score\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./main_text_figures/figure1.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2\n",
    "\n",
    "This figure shows the correlations between the Pop Score and some dimensions of the 2017 CHES data. The data can be downloaded from here,\n",
    "https://www.chesdata.eu/our-surveys, but we already stored it in the CHES_data directory. The data we refer to is the \"2017 Chapel Hill Expert FLASH Survey (CHES)\".\n",
    "\n",
    "Note that CHES data and our data did not perfectly match, hence first we map CHES data party names with the names used in our data (see the \"party_mapping\" dictionary) and then we merge it with our \"df_global_scores\" DataFrame.\n",
    "\n",
    "Some parties present in CHES have been excluded from the analysis. See main text for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pearsons_whole_stats(x,y):\n",
    "    \"\"\"\n",
    "    This function computes the pearson's correlation coefficient between two variables x and y.\n",
    "    It also computes the relative confidence interval from 5th to 95th percentile, and returns\n",
    "    the p-value of the null hypothesis that the correlation coefficient is 0.\n",
    "    \"\"\"\n",
    "\n",
    "    r, p = scipy.stats.pearsonr(x,y)\n",
    "\n",
    "    r_z = np.arctanh(r)\n",
    "    se = 1/np.sqrt(x.size-3)\n",
    "    alpha = 0.05\n",
    "    z = scipy.stats.norm.ppf(1-alpha/2)\n",
    "\n",
    "    lo_z, hi_z = r_z-z*se, r_z+z*se\n",
    "    lo, hi = np.tanh((lo_z, hi_z))\n",
    "\n",
    "    output = {\"coeff\":r, \"p_value\":p, \"lo\":lo, \"hi\":hi}\n",
    "    \n",
    "    return output\n",
    "\n",
    "\n",
    "def map_to_orientation(lrgen):\n",
    "    \"\"\"\n",
    "    This function classifies parties' political orientations using the \"lrgen\" field of CHES 2017\n",
    "    \"\"\"\n",
    "    \n",
    "    if lrgen<4: return \"Left-wing\"\n",
    "    elif lrgen<6: return \"Other\"\n",
    "    else: return \"Right-wing\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading CHES data..\n",
      "changing country identifiers...\n",
      "mapping CHES party names to our notation...\n",
      "merging dataframes..\n"
     ]
    }
   ],
   "source": [
    "print(\"reading CHES data..\")\n",
    "df_ches = pd.read_csv(\"./CHES_data/CHES_means_2017.csv\")\n",
    "df_ches = df_ches[['country', 'year','party','people_vs_elite', 'antielite_salience', \"lrgen\"]].copy()\n",
    "\n",
    "\n",
    "country_mapping = {\n",
    "    \"ger\":\"DE\",\n",
    "    \"it\":\"IT\",\n",
    "    \"fr\":\"FR\",\n",
    "    \"nl\":\"NL\",\n",
    "    \"esp\":\"ES\",\n",
    "}\n",
    "\n",
    "party_mapping = {\n",
    "    \n",
    "    \"DE\":{\n",
    "        \"CDU\":\"Christian Democratic Union-Christian Social Union\",\n",
    "        \"SPD\":\"Social Democratic Party of Germany\",\n",
    "        \"Grunen\":\"Alliance‘90-Greens\",\n",
    "        \"Linke\":\"The Left\",\n",
    "        \"AfD\":\"Alternative for Germany\",\n",
    "    },\n",
    "    \n",
    "    \"IT\":{\n",
    "        \"LN\":\"Northern League\",\n",
    "        \"UDC\":\"UDC\",\n",
    "        \"PD\":\"PD\",\n",
    "        \"CD\":\"Democratic Center\",\n",
    "        \"FdI\":\"Brothers of Italy\",\n",
    "        \"M5S\":\"M5S\",\n",
    "    },\n",
    "    \n",
    "    \"FR\":{\n",
    "        \"PCF\":\"French Communist Party\",\n",
    "        \"PS\":\"Socialist Party\",\n",
    "        \"EELV\":\"The Greens\",\n",
    "        \"LR\":\"Union for a Popular Movement\",\n",
    "        \"FN\":\"National Front\",\n",
    "        \"MODEM\":\"Democratic Mouvement\",\n",
    "        \"LREM\":\"Republic Onwards!\",\n",
    "        \"Insoumis\":\"Indomitable France\",\n",
    "        \"DLF\":\"Debout la France\",\n",
    "    },\n",
    "    \n",
    "    \"ES\":{\n",
    "        \"PSOE\":\"Spanish Socialist Workers’ Party\",\n",
    "        \"PP\":\"People's Party\",\n",
    "        \"IU\":\"United Left\",\n",
    "        \"Podemos\":\"We can\",\n",
    "    },\n",
    "    \n",
    "    \"NL\":{\n",
    "        \"CDA\":\"Christian Democratic Appeal\",\n",
    "        \"PvdA\":\"Labour Party\",\n",
    "        \"VVD\":\"People’s Party for Freedom and Democracy\",\n",
    "        \"D66\":\"Democrats‘66\",\n",
    "        \"GL\":\"Green Left\",\n",
    "        \"SGP\":\"Reformed Political Party\",\n",
    "        \"SP\":\"Socialist Party\",\n",
    "        \"CU\":\"Christian Union\",\n",
    "        \"PVV\":\"Party of Freedom\",\n",
    "        \"PvdD\":\"Party for the Animals\",\n",
    "        \"50PLUS\":\"50Plus\",\n",
    "        \"Denk\":\"DENK\",\n",
    "        \"FvD\":\"Forum for Democracy\"\n",
    "    },\n",
    "    \n",
    "}\n",
    "\n",
    "print(\"changing country identifiers...\")\n",
    "df_ches = df_ches[df_ches.country.isin(country_mapping.keys())].copy()\n",
    "df_ches[\"country\"] = [country_mapping[country] for country in df_ches.country]\n",
    "df_ches = df_ches.rename(columns={\"country\":\"nation\"})\n",
    "##############\n",
    "print(\"mapping CHES party names to our notation...\")\n",
    "for nation in party_mapping:\n",
    "    new_party = []\n",
    "    for party in df_ches.loc[df_ches.nation==nation, \"party\"].values:\n",
    "        try: new_party.append(party_mapping[nation][party])\n",
    "        except KeyError: new_party.append( \"missing\")\n",
    "    df_ches.loc[df_ches.nation==nation, \"party\"] =new_party\n",
    "\n",
    "df_ches = df_ches[df_ches.party != \"missing\"]\n",
    "##############\n",
    "print(\"merging dataframes..\")\n",
    "df_ches = pd.merge(df_ches,df_global_scores, on=[\"nation\", \"party\"], how=\"left\",suffixes=(\"_ches\",\"_score\") )\n",
    "df_ches = df_ches.dropna()\n",
    "df_ches[\"orientation_ches\"] = [map_to_orientation(lrgen) for lrgen in df_ches.lrgen.values]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pop Score-Chess comparison for Left-wing parties...\n",
      "Score vs people_vs_elite: \n",
      "\t Corr. coeff = 0.614 with CI [0.17,0.851], p-value = 0.011\n",
      "Score vs antielite_salience: \n",
      "\t Corr. coeff = 0.738 with CI [0.382,0.903], p-value = 0.001\n",
      "\n",
      "Pop Score-Chess comparison for Right-wing parties...\n",
      "Score vs people_vs_elite: \n",
      "\t Corr. coeff = 0.884 with CI [0.63,0.967], p-value = 0.0\n",
      "Score vs antielite_salience: \n",
      "\t Corr. coeff = 0.806 with CI [0.433,0.944], p-value = 0.002\n",
      "\n",
      "Pop Score-Chess comparison for Other parties...\n",
      "Score vs people_vs_elite: \n",
      "\t Corr. coeff = 0.816 with CI [0.33,0.96], p-value = 0.007\n",
      "Score vs antielite_salience: \n",
      "\t Corr. coeff = 0.934 with CI [0.712,0.986], p-value = 0.0\n",
      "\n",
      "Pop Score-Chess comparison for All parties...\n",
      "Score vs people_vs_elite: \n",
      "\t Corr. coeff = 0.753 with CI [0.568,0.866], p-value = 0.0\n",
      "Score vs antielite_salience: \n",
      "\t Corr. coeff = 0.809 with CI [0.658,0.898], p-value = 0.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orientations = [\"Left-wing\", \"Right-wing\",\"Other\", \"All\"]\n",
    "feats = [\"people_vs_elite\",\"antielite_salience\"]\n",
    "colours = [\"red\",\"yellow\",\"blue\",\"green\"]\n",
    "\n",
    "colours = sns.color_palette(\"tab10\", n_colors=4).as_hex()\n",
    "colours = [colours[3], colours[1], colours[0], colours[2]]\n",
    "\n",
    "Os = [\"L\",\"O\", \"R\", \"P\"]\n",
    "\n",
    "sns.set_context(\"notebook\", font_scale=1.5)\n",
    "sns.set_style(\"whitegrid\")\n",
    "fig, axs = plt.subplots(figsize=(10,8), ncols=2, nrows=2)\n",
    "\n",
    "\n",
    "for iii, orient in enumerate(orientations):\n",
    "    print(\"Pop Score-Chess comparison for {} parties...\".format(orient))\n",
    "    \n",
    "    if orient!=\"All\": df_ches_sub = df_ches[df_ches.orientation_ches==orient]\n",
    "    else: df_ches_sub = df_ches\n",
    "    \n",
    "    corr_coeffs = []\n",
    "    CI_low = []\n",
    "    CI_high = []\n",
    "    for feat in feats:\n",
    "        print(\"Score vs {0}: \".format(feat))\n",
    "        x = df_ches_sub[feat].values\n",
    "        y = df_ches_sub.score.values\n",
    "        res = pearsons_whole_stats(x,y)\n",
    "        res = {k:round(res[k],3) for k in res}\n",
    "        \n",
    "        corr_coeffs.append(res[\"coeff\"])\n",
    "        CI_low.append(res[\"coeff\"]-res[\"lo\"])\n",
    "        CI_high.append(res[\"hi\"]-res[\"coeff\"])\n",
    "       \n",
    "        print(\"\\t Corr. coeff = {0} with CI [{1},{2}], p-value = {3}\".format(res[\"coeff\"], res[\"lo\"], res[\"hi\"], res[\"p_value\"]))\n",
    "\n",
    "    print()\n",
    "    \n",
    "    ######################\n",
    "    \n",
    "    ax = axs.flatten()[iii]\n",
    "    y = range(len(corr_coeffs))\n",
    "    ax.errorbar(corr_coeffs,y,xerr = [CI_low,CI_high], color=colours[iii], fmt='none')\n",
    "    ax.plot(corr_coeffs,y,\"o\", color=colours[iii])\n",
    "\n",
    "\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(feats)\n",
    "\n",
    "    ax.set_xlim(-1.1,1.1)\n",
    "    ax.set_ylim(y[0]-0.5, y[-1]+0.5)\n",
    "    ax.vlines(0, y[0]-0.5, y[-1]+0.5, ls=\"dashed\", color=\"black\")\n",
    "    ax.text(-1.7,1.5,Os[iii], fontdict={\"size\":30})\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./main_text_figures/figure2.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 3 and Figure 4\n",
    "\n",
    "Figure 3 shows the correlations between the Pop Score and some dimensions of the 2018 Populism and Political Parties Expert Survey (POPPA) dataset. The data can be downloaded from here,http://poppa-data.eu/, but we already stored it in the POPPA_data directory. \n",
    "\n",
    "Figure 4 shows the correlation between the Pop Score and a latent populism variables that can be computed from the POPPA dataset following this work:\n",
    "\n",
    "Meijers, Maurits J., and Andrej Zaslove. \"Measuring populism in political parties: appraisal of a new approach.\" Comparative Political Studies 54.2 (2021): 372-407.\n",
    "\n",
    "\n",
    "Note that POPPA data and our data did not perfectly match, hence first we map POPPA data party names with the names used in our data (see the \"party_mapping\" dictionary) and then we merge it with our \"df_global_scores\" DataFrame.\n",
    "\n",
    "Some parties present in POPPA have been excluded from the analysis. See main text for details.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading POPPA dataset...\n",
      "changing country identifiers...\n",
      "mapping POPPA party names to our notation...\n",
      "merging dataframes..\n"
     ]
    }
   ],
   "source": [
    "print(\"reading POPPA dataset...\")\n",
    "df_poppa = pd.read_csv(\"./POPPA_data/poppa_and_latent_variable.csv\")\n",
    "df_poppa = df_poppa[[\"country_id\",\"party\",'peoplecentrism',\\\n",
    "         'manichean','indivisble','generalwill','antielitism',\"latent\", \"lroverall\"]]\n",
    "\n",
    "#############\n",
    "country_mapping = {\n",
    "    \"GE\":\"DE\",\n",
    "    \"IT\":\"IT\",\n",
    "    \"FR\":\"FR\",\n",
    "    \"NL\":\"NL\",\n",
    "    \"SP\":\"ES\",\n",
    "    \"AU\":\"AT\",\n",
    "\n",
    "}\n",
    "\n",
    "\n",
    "party_mapping = {\n",
    "    \n",
    "    \"DE\":{\n",
    "        \"CDU\":\"Christian Democratic Union-Christian Social Union\",\n",
    "        \"SPD\":\"Social Democratic Party of Germany\",\n",
    "        \"B90Grune\":\"Alliance‘90-Greens\",\n",
    "        \"Linke\":\"The Left\",\n",
    "        \"AfD\":\"Alternative for Germany\",\n",
    "    },\n",
    "    \n",
    "    \"IT\":{\n",
    "        \"LN\":\"Northern League\",\n",
    "        \"UDC\":\"UDC\",\n",
    "        \"PD\":\"PD\",\n",
    "        \"CD\":\"Democratic Center\",\n",
    "        \"FdlCN\":\"Brothers of Italy\",\n",
    "        \"M5S\":\"M5S\",\n",
    "    },\n",
    "    \n",
    "    \"FR\":{\n",
    "        \"PCF\":\"French Communist Party\",\n",
    "        \"PS\":\"Socialist Party\",\n",
    "        \"EELV\":\"The Greens\",\n",
    "        \"LR\":\"Union for a Popular Movement\",\n",
    "        \"FN\":\"National Front\",\n",
    "        \"MoDem\":\"Democratic Mouvement\",\n",
    "        \"LaREM\":\"Republic Onwards!\",\n",
    "        \"LFI\":\"Indomitable France\",\n",
    "        \"DLF\":\"Debout la France\",\n",
    "        \"NPA\":\"Nouveau Parti Anticapitaliste\"\n",
    "    },\n",
    "    \n",
    "    \"ES\":{\n",
    "        \"PSOE\":\"Spanish Socialist Workers’ Party\",\n",
    "        \"PP\":\"People's Party\",\n",
    "        \"Podemos\":\"We can\",\n",
    "    },\n",
    "    \n",
    "    \"NL\":{\n",
    "        \"CDA\":\"Christian Democratic Appeal\",\n",
    "        \"PvdA\":\"Labour Party\",\n",
    "        \"VVD\":\"People’s Party for Freedom and Democracy\",\n",
    "        \"D66\":\"Democrats‘66\",\n",
    "        \"GL\":\"Green Left\",\n",
    "        \"SGP\":\"Reformed Political Party\",\n",
    "        \"SP\":\"Socialist Party\",\n",
    "        \"CU\":\"Christian Union\",\n",
    "        \"PVV\":\"Party of Freedom\",\n",
    "        \"PvdD\":\"Party for the Animals\",\n",
    "        \"50Plus\":\"50Plus\",\n",
    "        \"DENK\":\"DENK\",\n",
    "        \"FvD\":\"Forum for Democracy\",\n",
    "    },\n",
    "    \n",
    "    \"AT\":{\n",
    "        'PILZ':\"Peter Pilz List\",\n",
    "        \"FPO\":\"Austrian Freedom Party\",\n",
    "        \"Gruene\": \"The Greens\",\n",
    "        \"NEOS\": \"The New Austria\",\n",
    "        \"OVP\": \"Austrian People’s Party\",\n",
    "        \"SPO\":\"Austrian Social Democratic Party\"\n",
    "    }\n",
    "    \n",
    "}\n",
    "\n",
    "##############\n",
    "print(\"changing country identifiers...\")\n",
    "df_poppa = df_poppa[df_poppa.country_id.isin(country_mapping.keys())].copy()\n",
    "df_poppa[\"country_id\"] = [country_mapping[country] for country in df_poppa.country_id]\n",
    "df_poppa = df_poppa.rename(columns={\"country_id\":\"nation\"})\n",
    "##############\n",
    "print(\"mapping POPPA party names to our notation...\")\n",
    "\n",
    "df_poppa[\"old_party\"] = [party for party in df_poppa.party]\n",
    "for nation in party_mapping:\n",
    "    new_party = []\n",
    "    for party in df_poppa.loc[df_poppa.nation==nation, \"party\"].values:\n",
    "        try: new_party.append(party_mapping[nation][party])\n",
    "        except KeyError: new_party.append( \"missing\")\n",
    "    df_poppa.loc[df_poppa.nation==nation, \"party\"] =new_party\n",
    "\n",
    "df_poppa = df_poppa[df_poppa.party != \"missing\"]\n",
    "##############\n",
    "\n",
    "print(\"merging dataframes..\")\n",
    "df_poppa = pd.merge(df_poppa,df_global_scores, on=[\"nation\", \"party\"], how=\"left\",suffixes=(\"_poppa\",\"_score\") )\n",
    "df_poppa[\"orientation_poppa\"] = [map_to_orientation(lroverall) for lroverall in df_poppa.lroverall.values]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pop Score-POPPA comparison for Left-wing parties...\n",
      "Score vs peoplecentrism: \n",
      "\t Corr. coeff = 0.81 with CI [0.49,0.938], p-value = 0.0\n",
      "Score vs manichean: \n",
      "\t Corr. coeff = 0.766 with CI [0.398,0.922], p-value = 0.001\n",
      "Score vs indivisble: \n",
      "\t Corr. coeff = 0.659 with CI [0.197,0.881], p-value = 0.01\n",
      "Score vs generalwill: \n",
      "\t Corr. coeff = 0.785 with CI [0.435,0.929], p-value = 0.001\n",
      "Score vs antielitism: \n",
      "\t Corr. coeff = 0.78 with CI [0.426,0.927], p-value = 0.001\n",
      "\n",
      "Pop Score-POPPA comparison for Right-wing parties...\n",
      "Score vs peoplecentrism: \n",
      "\t Corr. coeff = 0.856 with CI [0.625,0.949], p-value = 0.0\n",
      "Score vs manichean: \n",
      "\t Corr. coeff = 0.801 with CI [0.505,0.928], p-value = 0.0\n",
      "Score vs indivisble: \n",
      "\t Corr. coeff = 0.753 with CI [0.411,0.909], p-value = 0.001\n",
      "Score vs generalwill: \n",
      "\t Corr. coeff = 0.762 with CI [0.428,0.913], p-value = 0.001\n",
      "Score vs antielitism: \n",
      "\t Corr. coeff = 0.853 with CI [0.62,0.948], p-value = 0.0\n",
      "\n",
      "Pop Score-POPPA comparison for Other parties...\n",
      "Score vs peoplecentrism: \n",
      "\t Corr. coeff = 0.894 with CI [0.635,0.972], p-value = 0.0\n",
      "Score vs manichean: \n",
      "\t Corr. coeff = 0.758 with CI [0.289,0.933], p-value = 0.007\n",
      "Score vs indivisble: \n",
      "\t Corr. coeff = 0.865 with CI [0.551,0.964], p-value = 0.001\n",
      "Score vs generalwill: \n",
      "\t Corr. coeff = 0.814 with CI [0.419,0.95], p-value = 0.002\n",
      "Score vs antielitism: \n",
      "\t Corr. coeff = 0.755 with CI [0.283,0.932], p-value = 0.007\n",
      "\n",
      "Pop Score-POPPA comparison for All parties...\n",
      "Score vs peoplecentrism: \n",
      "\t Corr. coeff = 0.837 with CI [0.712,0.91], p-value = 0.0\n",
      "Score vs manichean: \n",
      "\t Corr. coeff = 0.751 with CI [0.577,0.86], p-value = 0.0\n",
      "Score vs indivisble: \n",
      "\t Corr. coeff = 0.691 with CI [0.486,0.823], p-value = 0.0\n",
      "Score vs generalwill: \n",
      "\t Corr. coeff = 0.74 with CI [0.56,0.853], p-value = 0.0\n",
      "Score vs antielitism: \n",
      "\t Corr. coeff = 0.803 with CI [0.658,0.891], p-value = 0.0\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orientations = [\"Left-wing\", \"Right-wing\",\"Other\", \"All\"]\n",
    "feats = ['peoplecentrism','manichean','indivisble','generalwill' ,  'antielitism']\n",
    "\n",
    "colours = sns.color_palette(\"tab10\", n_colors=4).as_hex()\n",
    "colours = [colours[3], colours[1], colours[0], colours[2]]\n",
    "\n",
    "Os = [\"L\",\"O\", \"R\", \"P\"]\n",
    "\n",
    "sns.set_context(\"notebook\", font_scale=1.5)\n",
    "sns.set_style(\"whitegrid\")\n",
    "fig, axs = plt.subplots(figsize=(10,12), ncols=2, nrows=2)\n",
    "\n",
    "\n",
    "for iii, orient in enumerate(orientations):\n",
    "    print(\"Pop Score-POPPA comparison for {} parties...\".format(orient))\n",
    "    \n",
    "    if orient!=\"All\": df_poppa_sub = df_poppa[df_poppa.orientation_poppa==orient]\n",
    "    else: df_poppa_sub = df_poppa\n",
    "    \n",
    "    corr_coeffs = []\n",
    "    CI_low = []\n",
    "    CI_high = []\n",
    "    for feat in feats:\n",
    "        print(\"Score vs {0}: \".format(feat))\n",
    "        x = df_poppa_sub[feat].values\n",
    "        y = df_poppa_sub.score.values\n",
    "        res = pearsons_whole_stats(x,y)\n",
    "        res = {k:round(res[k],3) for k in res}\n",
    "        \n",
    "        corr_coeffs.append(res[\"coeff\"])\n",
    "        CI_low.append(res[\"coeff\"]-res[\"lo\"])\n",
    "        CI_high.append(res[\"hi\"]-res[\"coeff\"])\n",
    "        print(\"\\t Corr. coeff = {0} with CI [{1},{2}], p-value = {3}\".format(res[\"coeff\"], res[\"lo\"], res[\"hi\"], res[\"p_value\"]))\n",
    "\n",
    "    print()\n",
    "    \n",
    "    ######################\n",
    "    \n",
    "    ax = axs.flatten()[iii]\n",
    "    y = range(len(corr_coeffs))\n",
    "    ax.errorbar(corr_coeffs,y,xerr = [CI_low,CI_high], color=colours[iii], fmt='none')\n",
    "    ax.plot(corr_coeffs,y,\"o\", color=colours[iii])\n",
    "\n",
    "\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(feats)\n",
    "\n",
    "    ax.set_xlim(-1.1,1.1)\n",
    "    ax.set_ylim(y[0]-0.5, y[-1]+0.5)\n",
    "    ax.vlines(0, y[0]-0.5, y[-1]+0.5, ls=\"dashed\", color=\"black\")\n",
    "    ax.text(-1.7,4.5,Os[iii], fontdict={\"size\":30})\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./main_text_figures/figure3.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t Corr. coeff = 0.845 with CI [0.726,0.915], p-value = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x468 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"notebook\", font_scale=1.5)\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "fig  = plt.figure(figsize=(9.5,6.5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "r = scipy.stats.pearsonr(df_poppa[\"score\"], df_poppa['latent'])[0]\n",
    "n = len(df_poppa[\"score\"])\n",
    "err_r = np.sqrt((1 -r*r)/(n-2))\n",
    "\n",
    "res = pearsons_whole_stats(df_poppa.score.values, df_poppa.latent.values)\n",
    "res = {k:round(res[k],3) for k in res}\n",
    "print(\"\\t Corr. coeff = {0} with CI [{1},{2}], p-value = {3}\".format(res[\"coeff\"], res[\"lo\"], res[\"hi\"], res[\"p_value\"]))\n",
    "\n",
    "\n",
    "r = round(r,2)\n",
    "err_r = round(err_r,2)\n",
    "lo = round(res[\"lo\"],2)\n",
    "hi = round(res[\"hi\"],2)\n",
    "\n",
    "s = r\"$\\rho = {0}$, with 5%-95% CI = $[{1},{2}]$\".format(r, lo,hi)\n",
    "reg_p = sns.regplot(x=\"latent\", y=\"score\", data=df_poppa,label=s,truncate=False ,ax=ax)\n",
    "\n",
    "\n",
    "#################\n",
    "\n",
    "def label_point(x, y, val, ax, offx = 0, offy=0,fontsize=15):\n",
    "    a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)\n",
    "    for i, point in a.iterrows():\n",
    "        ax.text(point['x']-.02+offx, point['y']+offy, str(point['val']),fontdict={\"fontsize\":fontsize})\n",
    "not_to_plot = [\"PVV\", \"D66\", \"MoDem\", \"SPD\",\"B90Grune\",\"PSOE\",\"PP\", \"PvdA\", \"SGP\",\"PS\", \"NEOS\",\"GL\",\"Gruene\", \"CU\",\"VVD\",\"SPO\", \"LN\"]\n",
    "df_poppa_to_plot = df_poppa[~df_poppa.old_party.isin(not_to_plot)]\n",
    "\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca())  \n",
    "\n",
    "#################\n",
    "\n",
    "to_plot_now = [\"PVV\"]\n",
    "df_poppa_to_plot = df_poppa[df_poppa.old_party.isin(to_plot_now)]\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca(), offx=-1,offy=-0.035)  \n",
    "\n",
    "#################\n",
    "\n",
    "to_plot_now = [\"D66\"]\n",
    "df_poppa_to_plot = df_poppa[df_poppa.old_party.isin(to_plot_now)]\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca(), offx=0,offy=0.01)  \n",
    "\n",
    "#################\n",
    "\n",
    "to_plot_now = [\"PSOE\"]\n",
    "df_poppa_to_plot = df_poppa[df_poppa.old_party.isin(to_plot_now)]\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca(), offx=-1.7,offy=-0.04)  \n",
    "\n",
    "#################\n",
    "\n",
    "to_plot_now = [\"PP\"]\n",
    "df_poppa_to_plot = df_poppa[df_poppa.old_party.isin(to_plot_now)]\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca(), offx=0.1,offy=-0.04)  \n",
    "\n",
    "#################\n",
    "\n",
    "to_plot_now = [\"Gruene\"]\n",
    "df_poppa_to_plot = df_poppa[df_poppa.old_party.isin(to_plot_now)]\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca(), offx=-1.7,offy=0.015,fontsize=14)  \n",
    "\n",
    "#################\n",
    "\n",
    "to_plot_now = [\"GL\"]\n",
    "df_poppa_to_plot = df_poppa[df_poppa.old_party.isin(to_plot_now)]\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca(), offx=-1.2,offy=-0.015,fontsize=15)  \n",
    "\n",
    "#################\n",
    "\n",
    "to_plot_now = [\"VVD\"]\n",
    "df_poppa_to_plot = df_poppa[df_poppa.old_party.isin(to_plot_now)]\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca(), offx=-0.6,offy=0.01,fontsize=15)  \n",
    "\n",
    "#################\n",
    "\n",
    "to_plot_now = [\"SPO\"]\n",
    "df_poppa_to_plot = df_poppa[df_poppa.old_party.isin(to_plot_now)]\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca(), offx=0.3,offy=-0.01,fontsize=15)  \n",
    "\n",
    "#################\n",
    "\n",
    "to_plot_now = [\"LN\"]\n",
    "df_poppa_to_plot = df_poppa[df_poppa.old_party.isin(to_plot_now)]\n",
    "label_point(df_poppa_to_plot[\"latent\"], df_poppa_to_plot[\"score\"], df_poppa_to_plot.old_party, plt.gca(), offx=-1.0,offy=-0.0,fontsize=15)  \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "############\n",
    "to_plot_now = [ \"MoDem\", \"SPD\",\"B90Grune\", \"PvdA\", \"SGP\",\"PS\", \"NEOS\",\"CU\"]\n",
    "colors = sns.color_palette(\"Set2\", n_colors=len(to_plot_now)).as_hex()\n",
    "\n",
    "axs_p = []\n",
    "labels_p = []\n",
    "for iii,party in enumerate(to_plot_now):\n",
    "    df_poppa_to_plot = df_poppa[df_poppa.old_party==party]\n",
    "    ax_p, = ax.plot(df_poppa_to_plot.latent.values, df_poppa_to_plot.score.values,'o', color=colors[iii])\n",
    "    axs_p.append(ax_p)\n",
    "    labels_p.append(party)\n",
    "\n",
    "############\n",
    "\n",
    "l_p = plt.legend(axs_p, labels_p, loc=\"upper left\", bbox_to_anchor=(0.0,0.88),ncol=3, title=\"Unlabelled Points\",fontsize=14)\n",
    "plt.setp(l_p.get_title(),fontsize=14)\n",
    "\n",
    "plt.gca().add_artist(l_p)\n",
    "\n",
    "ax.legend(loc=\"upper left\")\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"Latent populism variable\")\n",
    "ax.set_ylabel(\"(Pop.) Score\")\n",
    "ax.set_xlim(10,41)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./main_text_figures/figure4.jpg\", dpi=300)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 5\n",
    "\n",
    "Figure 5 shows the average Pop. Score for each nation in each year. We simply aggregate over the \"df_scores_in_time\" dataframe, computing average and standard error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def err(x):\n",
    "    if x.size<=1: return 0.0\n",
    "    return np.sqrt(x.var()/x.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores_in_time_nations = df_scores_in_time.groupby([\"nation\",\"year\"]).aggregate({\"score\":[np.mean,err]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3c+edd6IoStMX39w2zocX+sPMOPXtxvnRrqjJtYVpweWLFpF13li29ulL1nljKV+0KNolNcp1112H1Wrll19+oby8PNrltBhbly3hjT/ewPNXT+SNP97A1mVLol1SjR9//JGysjImTJiA3W7n3HPPZf369ezZsyfapQkhhBD1LNlWwOWvr6DC62fWDUOZOqJTtEsSdZwSz9IVRSEYDDJt2rSI5imPP/44W7duZcOGDaxbt44hQ4ZEscpjtHE+LLoT/B714/J96scAA6+MXl11ZGVl8fLLL9c7fv311x9xvVP1qAfA4MGDm62+lqB80SJyH30MxesFIHDwILmPPgZA7MSJ0SztqKxWK3379mXt2rVkZmZGTA+fO3dug2sU27dvz5QpU05mmSfV1mVL+O6NVwj4qgBwFhXy3RuvANDnrDHRLA2onXY6Mfy9NXHiRL799lsWLlzIXXfdFc3ShBBCiAjvrMjhiS+2kB5rYdaMYXRPrT9oJKIr6kGxetSwemSxrupjDofjqI9RWlrKmDH1n6ide+65bNiwgc2bN5+8oPjrB7DhvRN7jP1rIFgVeczvgc/+BOveOf7HHXwdDLrmxGoLy8rKIisrq97xyZMnRwTFdevW1QTK0tJSli5dyv79+znnnHMabIbTUpQtXEj5J0fuuHs0nt9+Q/H5Io4pXi+5f3mEsvkfHddjxl42hbhJk06orsZKSUkB1P+3uubOndvg9cOGDWuxQXHL0sVs/vF/J/QYuTu2Ewz4I44FfFV8+/q/2fjDt8f9uP3P/R39zjl8J+HGKCkpYdmyZZx22ml06NABgHPOOYeYmBg+++wz7rzzTpnKI4QQIupCIYWZi7bw7so9nNExnjevH0KCzRjtskQDoh4U665D7NevX8S5I61frKtz586UlpY2GCirj1VVVdU716IdGhKPdjwKLrroIl544YWjXrd+/XrWr18fcWzUqFG89tprp3yjoUND4tGOtxZfffVVm2xmc2hIPNrxk+nzzz/H7/dz8cUX1xwzGo2cf/75fPzxx6xatYoRI0ZEsUIhhBBtXaU3wB8/WMeP24u4dFA7nr18IEb9qf1csDWLelAcMmQIb775JitXruTCCy+MOLdy5cqaa45k+PDhbNiwgezs7HphMzs7G4D09PQmrPooBl1z4qN2L/RXp5seKrYD3PDliT32SXbzzTdz3333EQqF2LdvH88//zzffvstzz77LA8++GC0yzusuEmTTnjkLuu8sQQaaKakb9eOTnPePaHHPhkKCwsBTomtMPqdM/aER+3e+OMNOIsK6x13JCVz1eNPn9Bjn6iFCxei0+m46KKLIo5PnDiRjz/+mIULF0pQFEIIETUHy9zcMGsN2/Nd3D2uB3eN7SEzXVq4qDezGTVqFGlpaXz++efs2rWr5nh2djaLFi2ib9++NVtj+P1+srOz63UxnTJlCgaDgTfffDNiCmtOTg4LFizAarVy1llnnZwvqKmMfQwMh2wwarCox1sprVZLp06d+Ne//kWfPn2YPXs2v/32W7TLalYp99yN5pB9IjVmMyn33B2dgo6Bx+Nhy5YtaLXaI25P05acdfV09EZTxDG90cRZV08/zD1Ojm3btrF161aCwSCjR4+mV69eNbfrr78eULcRcrvdUa1TCCFE2/TrvlIufWU5e4rdvHT1IO4e11NCYisQ9RFFo9HIzJkzuf3227n66qtrpk198cUXhEIh/vrXv9Zcm5+fz0UXXcSwYcOYM2dOzfFOnTpx11138dxzz3HJJZcwZswYPB4P3333HR6PhyeeeKL1jYhUN6xZ/Dco3w+xGWpIbCGNbE6EXq/n//7v/5gxYwYvvPACs2fPjnZJzaa6YU3BCy8SyM1Fn55Oyj13t/hGNgBz5szB7XZz5plnEhsbG+1yWoTqhjXL5r2Ls7gIR2ISZ109PeqNbKqb2Jx11lmkpqbWO5+dnc2GDRv47rvvmHSS1rcKIYQQAF9uPMif5/+GzaTnvd8PZ0jnhGiXJBop6kERYMyYMcyaNYtXX32VBQsWoNFoOP3007nnnnvqTSU9nJtvvpn27dsza9YsPvnkEzQaDf379+fmm2/m7LPPbuavoJkMvPKUCIYNGTlyJIMHD2blypVs2LDhlO5+GjtxYqsIhtUCgQAffvgh//73vzGZTNx7773RLqlF6XPWmKgHw7oCgQCLFi3CYrHw4osvNrjV0I4dO5g4cSILFiyQoCiEEOKkUBSFV5fs5PnvdtA9xc7sG4bSPt4a7bLEMWgRQRFgxIgRR10/k5GRwfbt2w97/qKLLqq3Pke0XLfffjs333wzr776Kv/973+jXU6bVHeLE6/XS15eHqtXr6agoICEhAT++c9/NvhizeG2xwC4+uqrSU5Obta6Ra2ffvqJ4uJiLr744gZDIkDPnj3p27cvq1at4uDBg7Rr1+4kVymEEKIt8QeC/N8nm/h0wwHO7J7I69edgd1siHZZ4hi1mKAo2p6zzz6bAQMGsGzZMjZu3MjAgQOjXVKbU3eLE6vVSlxcHH379uXWW2/l0ksvPezWNIfbHgNg3LhxEhRPooULFwIcdaRw0qRJZGZmsnDhQm6//fbmL0wIIUSbVOr2ccu7a1mTU8p1Izryt0v6o9XKesTWSKMoihLtIlqS/fv3M3bsWBYvXkxGRsYRr926dSt9+vQ5SZUJIUTLI78HhRBCVNtV6OKG2WvYX+LmLxP6cOOZXU/K5wTomtzwrBpxeEfLPTKiKIQQQgghhDghK7KLuP29dfiDCm9MG8LYvvWbq4nWRYKiEEIIIYQQ4rh9sHovj362mWS7iQ9mDKVPeky0SxJNQIKiEEIIIYQQ4pgpisJTX2/jjZ92MaB9LG/NGEKKw3z0OzYhmXLafCQoCiGEEEIIIY6J1x/gzg9+5bvMfMb3S+WlqwdhNki0OJXI/6YQQgghhBCi0fIrPNw0ey2bD1bwh3O78cD4Xmg00tn0VCNBUQghhBBCCNEomQcruHH2Goorq3jm8gFcOaRjtEsSzUSCohBCCCGEEOKovtuSx90f/opRp+XdG4cxsltStEsSzUiC4glSFEWG2oUQbZJswyuEEG3Hf5ft4smvttIp0crbM4bSJUmayJzqJCieAJ1Oh9/vx2g0RrsUIYQ46fx+PzqdLtplCCGEaEaBYIhHP9vMB6v3MaxLAm9MO4M4qzz3bQskKJ4Ah8NBRUUFSUky7C6EaHsqKipwOBzRLkMIIUQzcXr83PreOlZkF3PFGRk8Obk/Br28QNhWSFA8AQkJCezduxeAmJgYDAaDTEMVQpzSFEXB7/dTUVFBaWkpHTtKEwMhhDgV7S2p5Ia317CrqJL7x/fij2O6R7skcZJJUDwBJpOJjh07UlJSQk5ODsFgMNolCSFEs9PpdDgcDjp27IjJZIp2OUIIIZrY2pwSbpmzFo8vxCtTBzNhQLtolySiQILiCTKZTKSnp5Oenh7tUoQQQgghhDghCzbs58FPNhFrMfDhrcMYmBEX7ZJElEhQFEIIIYQQoo1TFIUXvt/BvxfvpHeag7dnDKFdnDXaZYkokqAohBBCCCFEG+YLBPnz/N9YtDGXMb2SefXa07GaJCa0dfIdIIQQQgghRBtV4vJx0ztr2LCvjBtGd+bRCX3RaqU5o5CgKIQQQgghRJuUle/khtlryCv38sSl/Zg2snO0SxItiARFIYQQQggh2pifdhTyx/fXA/D2jKGc3TM5yhWJlkaCohBCCCGEEG3InJU5/HXRFtJiLbx9/VB6pjmiXZJogSQoCiGEEEII0QaEQgp/+yKT2StyGNwhjreuH0qC3RjtskQLdVxB0e12k5OTg9vtZsiQIU1dkxBCCCGEEKIJuasC/PH99SzZXsjEgek8f+VpGPW6aJclWjDtsVxcWFjIXXfdxbBhw7jsssuYPn16zbl169YxefJk1q1b1+RFCiGEEEIIIY5PbpmHKa+tYMn2Qu4c251/XzNYQqI4qkaPKJaUlHDVVVdx8OBB+vTpg9/vJzs7u+b8gAED2LdvH9988w1nnHFGsxQrhBBCCCGEaLzf9pdy8zvrKPf4eeGq05g8OCPaJYlWotEjiv/v//0/Dh48yNNPP82CBQsYP358xHmj0ciQIUNYs2ZNkxcphBBCCCGEODZfb8rl6v+sIhAKMff3wyUkimPS6KC4ZMkSRo8ezaRJkwDQaOpvxNmuXTvy8/ObrDghhBBCCCHEsXt1yU5un7ue9nFmPv/jmQzpnBDtkkQr0+igmJ+fT+/evY94jcViweVynXBRQgghhBBCiGPnDwS5/6PfePbb7YzslsiC20eTkWCNdlmiFWr0GkWbzUZJSckRrzlw4ABxcXEnWpMQQgghhBDiGJW5fdwyZx2rd5dwzbAOPHFpf/S6Y+pdKUSNRgfF/v37s2zZMrxeL2azud75kpISli1bxujRo5u0QCGEEEIIIcSR7S5yccOsNewtcfOXi/pw89ldo12SaOUa/RLDNddcQ1FREXfddRdFRUUR5woKCrjvvvtwu91cc801TV6kEEIIIYQQp4Kc8hxyynOa9DFXZhcx+dUVFDireP26MyQkiibR6BHFcePGcd111/Hee+9x9tlnY7fbAbjgggvYt28fwWCQG264gZEjRzZbsUIIIYQQQohaH63dx8MLNpFoM/H+zcPp2y422iWJU0SjgyLAI488wsCBA5k9ezZbt25FURRycnLo2bMnN954Y01HVCGEEEIIIUTzURSFZ77dzms/ZtO/XQxvzRhCaowl2mWJU0ijg+LBgwcxGAxccsklXHLJJXi9XsrLy3E4HFit0klJCCGEEEKIk8HrD3D3vN/4Zkse5/dN5d/XDMJsOKbxHyGOqtFrFMeOHcu//vWvmo/NZjOpqakSEoUQQgghhDhJCpxernj9F77ZksctZ3flP9POkJAomkWjv6tiYmKIj49vzlqEEEIIIYQQh7E1t4IbZ6+h0FXFU1MGcM2wjtEuSZzCGh0UTzvtNLZu3dqctQghhBBCCCEasHhrPnd+sAGDTsO7Nw5jVLekaJckTnGNnnr6pz/9ibVr1/LRRx81Zz1CCCGEEEKIOt7+eRe3vLuWJIeJT28fLSFRnBSNHlH86aefGDZsGI899hgffPABAwYMIDk5ud51Go2GP/7xj01apBBCCCGEEG1NKKTw6GebmbtqL0M7x/PG9CHEW43RLku0EY0Oiq+88goajQZFUcjMzCQzM7PB6yQoCiGEEEIIcWJcXj9/mLuOZVnFTBncnn9eNgCDXhftskQb0uig+NRTTzVnHUIIIYQQQgjgQKmb62etJrugkvvO78kfx3RHo9FEuyzRxjQ6KE6ePLk56xBCCCGEEKLNW5tTwq1z1lFZFeDlawdx8cD20S5JtFGy6YoQQgghhBAtwGe/HuD/Pt6Iw6xn3q0jGNRBtqYT0XPMQdHn8/H999+zZcsWnE4ndrud/v37M3bsWEwmU3PUKIQQQgghxClLURReWpzFi99n0SvVzqwZQ2kXb412WaKNO6ag+Msvv3DfffdRXFyMoig1xzUaDYmJiTz77LOMHDmyyYsUQgghhBDiSHLKcwDoHNs5qnUcK18gyH0fbeTz3w5ybq8kXr3mDGxmmfQnoq/R34U7d+7ktttuw+v1cu655zJ69GhSUlIoKChgxYoVLFmyhD/84Q989NFH9OjRozlrFkIIIYQQotUrqfRx8ztrWbe3lOkjOzFzYj+0WmlaI1qGRgfF1157DZ/Px3/+8x/OOeeciHPTpk1j6dKl3H777bz++us8//zzTV6oEEIIIYQQp4qd+S5ueGc1uWUeZk7sy4zRXaJdkhARGh0UV61axbhx4+qFxGrnnHMOY8eOZdWqVU1WnBBCCCGEEKeatbtd/G3hdhTgzelDGdM7JdolCVGPtrEXlpWV0aXLkV/p6NKlC2VlZSdakxBCCCGEEKekz9eX8OD8PTjMBj6+bZSERNFiNXpEMS4ujt27dx/xmpycHOLi4k60JiGEEEIIIU4piqLwxJdbefvnXHqnm3nvplEk2c3RLkuIw2r0iOKwYcNYvHgxS5cubfD88uXL+f777xk2bFiTFSeEEEIIIURr564K8Pt31vL2z7s5p7eDf1/XWUKiaPEaPaJ42223sXjxYm677TbOPfdcRo4cSVJSEkVFRaxatYoffvgBo9HIrbfe2pz1CiGEEEII0WrkVXi4cdYaMnOd/HFMdy4fbkSjkc6mouVrdFDs2bMnr776Kvfffz9Llizhxx9/rDmnKArx8fH885//pFevXs1RpxBCCCGEEK3KpgNl/P6dtZS6ffzryoFMOb1DzX6PQrR0x7Sb55lnnsmSJUv4/vvv2bJlC06nE4fDQb9+/Rg7diwWi+W4ili1ahWvvPIKmzdvRqfTMWjQIO6991769u17zI/lcrmYOHEiBw8e5KKLLuKFF144rpqEEEIIIYQ4Xt9szuPeD3/FZNAy96bhDO2SGO2ShDgmxxQUAcxmMxdffDEXX3xxkxTw448/8oc//AGHw8HkyZMB+OKLL7jmmmuYM2cOAwcOPKbHe+6556TzqhBCCCGEiJrXl2bzzDfb6JxoY9YNQ+mUaIt2SUIcs0Y3s2kOPp+Pxx9/HJPJxLx583jsscd47LHH+OCDD9BoNMycOfOYHm/NmjXMmzePu+66q3kKFkIIIYQQ4jACwRD3f/wbT3+9jeFdEljwx9ESEkWr1eigOHv2bIYPH05+fn6D5/Pz8xk+fDjvvfdeoz/5ihUryMvL45JLLqFr1641x7t168bEiRPZsmULmZmZjXosr9fLI488wrhx4xg3blyjaxBCCCGEEOJElXv8XPfWKj5au5+rh3Zgzk3DibUYol2WEMet0UHx22+/pXv37qSmpjZ4PjU1lZ49e/LNN980+pOvXbsWgJEjR9Y7V32s+pqjeemllygqKuLRRx9t9OcXQgghhBDiRO0prmTSqz+zencJD13Ym6cvG4heF9WJe0KcsEZ/B+/Zs4eePXse8ZoePXqwe/fuRn/yvXv3AtCxY8d656qPVV9zJBs3buSdd97hz3/+82GDrBBCCCGEEE1t1a5iJr26nPzyKl6bega3ntMt2iUJ0SQa3czG6XQSExNzxGscDgcVFRWN/uQulwsAu91e71z1MafTecTH8Pv9/OUvf+G0007jmmuuafTnFkIIIYQQ4kR8vHYfDy/cRLzVyHs3Dadf+9holyREk2l0UExKSiIrK+uI12RlZREfH3/CRR2L//znP+zevZuFCxfK5qVCCCGEEKLZKYrCM99u57Ufs+mb7uDtG4aSFnN828QJ0VI1eurpkCFD+Omnnw7bXGbz5s0sXbqUoUOHNvqTV48aVo8s1lV9zOFwHPb+u3bt4vXXX+eWW26he/fujf68QgghhBBCHI8qf5Db567ntR+zGds7hY9vGyUhUZySGh0Ub7rpJgCmTp3K//t//48tW7ZQVFTEli1bePXVV5k2bRoajabmusY40jrEI61frLZr1y78fj+vvvoqvXr1qrmNHTsWgK+++opevXpx++23N7omIYQQQgghGlLo9HLFf1by9eY8bj6rC/+9fghW0zFvSy5Eq9Do7+zevXvz97//nUceeYSXX36Zl19+ueacoijo9Xr+/ve/07dv30Z/8iFDhvDmm2+ycuVKLrzwwohzK1eurLnmcNq3b8/ll19e77jb7earr76iQ4cODB8+/JhqEkIIIYQQ4lDb85zcOHsN+RVenpzcn2uHd4p2SUI0q2N6CWTSpEmcdtppzJ07l99++42KigpiYmIYNGgQ11xzTcReiI0xatQo0tLS+Pzzz5kxY0bN/bOzs1m0aBF9+/atCXl+v5+9e/disVho164dAH369OEf//hHvcfdv38/X331FQMGDGjwvBBCCCGEEI31w9YC7py3Aa0GZt0wlLN6JEe7JCGa3TGPlXfp0oVHHnmkST650Whk5syZ3H777Vx99dVcfPHFAHzxxReEQiH++te/1lybn5/PRRddxLBhw5gzZ06TfH4hhBBCCCGOZNby3fzjy0zS4yzMun4Y3VPrd+sX4lTUJJOqQ6EQWu3xbSo6ZswYZs2axauvvsqCBQvQaDScfvrp3HPPPfTr168pyhNCCCGEEOKYhEIKjy/awpyVexjSKZ43pg8hwWaMdllCnDSNDoqbN29m/fr1TJo0qWY/xcrKSv7yl7/www8/YDAYuPXWW7nllluOuYgRI0YwYsSII16TkZHB9u3bG/V4x3KtEEIIIYQQdVV6A9z+/jqW7iji0kHtePbygRj1umiXJcRJ1ehhwLfeeos333yzJiQCvPDCC3zzzTfo9Xqqqqp44YUX+Omnn5qlUCGEEEIIIZrbwVI3k19bztIdRdw9rgcvXjVIQqJokxodFLds2cKwYcNqPg4EAnz22Wf069ePFStWsHjxYuLi4pg7d26zFCqEEEIIIURz2rC3lEtfXc7eYjf/vnoQd4/riUajiXZZQkRFo6eeFhcXk5aWVvNxZmYmTqeTq666CrPZjNls5rzzzmPFihXNUqgQQgghhBDNZdFvB7j/o43YTHrm3jycMzolRLskIaKq0UExFAoRCoVqPl6/fj0ajSZilDEpKYni4uKmrVAIIdqaop3q26Tu0a1DCCHaAEVReOWHnfzrfzvonmJn9g1DaR9vjXZZQkRdo4Nienp6RIOYn376iaSkJDp37lxzrKioKGINoxBCCCGEEC2VPxDkgU82smDDQc7qkchrU8/AbjZEuywhWoRGB8XzzjuPt956i6effhqTycQvv/zCFVdcEXFNTk4O7dq1a/IihWhqZfluAOJS5RVDIYQQoi0qdfu4+Z21rN1TynUjOvG3S/qh1cp6RCGqNToo3njjjXz33XfMnj0bUEcY//SnP9WcLyoq4tdff2X69OlNXqQQQgghhBBNJbvAxQ2zV3Og1MPjF/flhjO7RLskIVqcRgfFhIQEFi1aVNOsZujQodjt9przZWVlPPDAA5x55plNX6UQQgghhBBNYPnOIm5/bx1BBd6YPoSxfVKjXZIQLVKjgyKAyWRizJgxDZ7r3r073btL4wUhhBBCCNEyvb9qD499voUUu4m3Zwyld7r01hDicI4pKAohhBBCCNHaKIrCk19t481luxjQPpa3Zwwh2WGOdllCtGgSFIUQQgghxCnLXRXgrnkb+N/WAi7ol8aLV5+G2SBPgYU4GvkpEUIIIYQQp6T8Cg83zl7LloMV/OHcbjwwvhcajXQ2FaIxJCgKIYQQQohTzpYD5dz4zhpKKn08e/lArhjSIdolCdGqSFAUQgghhBCnlO+25HH3h79i1GmZc9NwRnRNjHZJQrQ6EhSFEEIIIcQp442fsnn66210SrQy64ZhdE60RbskIVolCYpCCCGEEKLVCwRDvPBtLl/+VsbwLgn8Z9oZxFmN0S5LiFZLgqIQQgghhGjVnB4/98/by4a9lVxxRgZPTu6PQa+Ldln1fLnrS55f+zxFniLSbGncdfpdTOg6IdplCdEgbVM9UG5uLtOnT+f6669vqocUQgghhBDiiPaWVDLp1eX8ureSm89N4dkrTmuxIXHmipkUegpRUMitzGXmipl8uevLaJcmRIOabETR4/GwevVqaTkshBBCCCFOijW7S7h1zlo8/hAzJ2dwTu/YaJd0WC+tfwlv0BtxzBv08tL6l2RUUbRITRYUU1JSeOqpp5rq4YQQQgghhDisT9fv56FPNxFrMfDhjcOIcZRFu6QaLp+LrcVb2Va6jazSLLLLs8mtzG3w2rzKvJNcnRCN02RB0W63M3ny5KZ6OCGEEEIIIepRFIXn/7eDV37YSe80B7NmDCU9zkJOedlJr8UX9JFVmsW2km3sKN1Bdnk2OWU55Hvya64x6Ux0dHTEpDNRFayq9xhptrSTWbIQjSbNbIQQQgghRKtQ5Q/y5/m/8cWmXMb0SubVa0/Hamr+p7MhJcRe5162Fm9lR+kOskqz2F2xmwPOAwSVIABatLS3t6dnQk8uiruIHvE96JfQj85xndFqtDVrFA+dfuowOCj1lhJvjm/2r0OIYyFBUQghhBBCtHglLh83zl7Dr/vLuHF0Zx6Z0Bettul7YxS4C9havJXtpdvJKs1iV/ku9lbsjQh4yZZkOsd2ZlT6KHrE96B3Qm96xffCpDcd9nGr1yHW7XraN6EvP+77kcs+v4ynznqK4enDm/zrEeJ4NTooPvTQQ0e9RqvV4nA46NatG+eddx6JiYknVJwQQgghhBA78pzcOHsNeRVenri0H9NGdj7hx3T5XGQWZ7KtZBs7y3aq00bLc6jwVdRc4zA46BzbmfGdx9Mjvge94nvRJ6EPsebja5ozoesE+iX2A6BzrPo1rM5dzUPLHuLW727lpoE38cdBf0SrabKNCY5f0U71bVL36NYhoqbRQXHBggU1HU0VRal3XqPR1BzXaDQ88cQT3HfffUyfPr2JShVCCCGEEG3N0u0F/OmDDQC8fcNQzu6RXO+aI+1P6Av42FG2g23F28gqy2Jn2U5yynMo8BTU3L96HeHI9JF0i+tGr4Re9E3sS6o1tdk7+g9LH8bHl3zMw8se5o2Nb7A2by3PnP0MqbbUZv28QhxNo4Piu+++y7vvvsvSpUuZNGkSZ5xxBomJiRQXF7N27Vo+++wzzj33XC6++GK2bNnCnDlzeOqpp+jSpQtnnXVWc34NQgghhBDiFPTuihz+9sUW0mMtvD1jKD1SHfWuOXTtX25lLn9Z9hdmb56NO+DmgKv+OsLeCb2ZEDeBnvE96ZvQt2YdYbTEm+P5f+P+H+9mvstL61/i8kWX88ToJzi3w7lRq0mIRgfF3Nxcli9fzkcffUTv3r0jzk2aNInrrruOq6++mvPPP597772XCy+8kMsvv5w5c+ZIUBRCCCGEEI0WCin87YtMZq/I4fSOcfx3+lAS7MYGr31+7fP1GsQECbKjdAdD0oYwqt0oesb3pFdCr6OuI4wmjUbD9f2u54zUM3hg6QPc8cMdXNv7Wu4bch8GnSHa5Yk2qNFB8Z133uGCCy6oFxKr9e7dmwsvvJBZs2YxceJE+vTpw9lnn82vv/7aVLUKIYQQQohTnLsqwB/fX8+S7YVcclo6z11xGka9LuIaRVH4JfcX3st8j0JPYYOPo6Dw1vi3TkbJTap/Un8+uuQjZq6Yyfvb3mdDwQaeO+c5OsZ0jHZpoo1p9Bj7rl27SElJOeI1KSkp7Nq1q+bjzp0743Q6j786IYQQQgjRZhwsczP5tRUs2V7IXWN78NLVgyNCoifgYW7mXCZ9Nolb/ncLa/PXYtFZGnys1rw/oc1g49lznmXmyJnsrtjNlYuuZFH2omiXJdqYRo8omkwmNm/efMRrNm/ejMlUO5zv8/mw2WzHX50QQgghhGgTfttfys3vrKPc4+fFq05j0uCMmnN7K/YyJ3MOX+76EqffSUdHR+49416u6HkFS/cvrbc/oVln5q7T74rGl9GkLut5GYOTB/PnpX/m4Z8f5pfcX3hkxCNY9A2HYyGaUqOD4qhRo/jmm2948803ueGGG9Dra+8aCASYNWsWK1as4IILLqg5np2dTVpa6301RwghhBBCNL8vNx7kz/N/w2rSMff3wxnSOQFFUVh2YBlzM+eyMnclACPSRzC1z1TOzji7phtpQ/sT1u162tp1je/Khxd/yJOrn+STrE/YVLSJZ89+ll4JvaJdmjjFNToo3nvvvaxcuZJ//etfvPfee5x22mk1XU9/++03CgoKiImJ4d577wWgoKCANWvWMG3atGYrXgghhBBCtG6v/JDF89/toFuyjdk3DCPOHuKdLe8wf8d89lbsxW6wc2WvK5nWdxqdYjo1+BgN7U94KjHqjcwcNZOR7Uby1xV/ZepXU7lvyH1c3fvqaJcmTmGNDoodOnTgww8/ZObMmaxcuZLvvvsu4vzIkSN57LHH6NChA6CuV9yyZUvTViuEEKe6jfPhf4+CMx9iM2DsYzDwymhXVc+uQhcAXZPtUa5ECNFa+QNB/u/TTXy6/gCjuyfy0CUJvLXtWb7a/RXugJsusV34v6H/x5QeU7AarNEut0UY33k8/RP7c9/S+/jHqn+wKncVT4x+Arux6X8XlxX5AYhLavKHFq1Eo4MiQKdOnZg1axb5+flkZmbidDpxOBz06dNHppgKAMry3QDEpcovdCGO2cb5sOhO8HvUj8v3qR9DiwyLQghxvMrcPm5+dy1rcko4d1Ap2rhPuebr1WjRMrr9aK7rcx0j241s9s3uW6P2jvbMuXAOL254kXe3vEtmcSbPnP0Mp6WcFu3SxCnmmIJitdTUVFJTU5u6FiGEaNsW/602JFbze9TjEhSFEKeI3UUups9aRl7wF9L7rmFd1T5iSmOY2mcq0/pMo72jfbRLbPH0Oj33DbmPYWnDeHT5o8z4Zga3D7qd3w/4vYRr0WQaHRSvu+46Lr/8csaPH4/FIp2WhBCiyZXvP8zxffDprZDUE5J7Q2pfiO8M8mRACNHKzP9tNU8smUPQsR2TsZD02C5c1esRJnWfhElvOvoDiAhnZ5zNxxM/5sFlD/LvDf9mdd5qnj7raRItidEuTZwCGh0U165dy7p16/jb3/7GhRdeyOTJkxkyZEhz1iaEEG1LbIYaCg+lNcDWL8Dvqj2mt6hhMbEbJPaAlN6Q0lcNknrjSStZNA2Zti9OZSElxP/2/I8Xf5nDnvIDYIVh7ftx++l/Z1j6sGiX1+olW5P57/n/5T8b/8Prv73OZYsu46kzn2Jku5HH/6Ab5+P45lG07pa9Xl40r0YHxUWLFvHJJ5/UvP3000/p2LEjU6ZMYdKkSTIVVbQaO1blsfzTnbjLfdgTTIy8tBs9h8saW9ECjH0sco0igMECE/8NA66A8gNQsBkKtkHhDijZCftWw7Yvaq/X6CCmfThAdofkXpDcB1L7gTX+5H9NQog2q9Rbyrxt8/gk6xNyy50EfUnEa0by5pRb6Z/acPdScXw0Gg23nXYbw9KG8cBPD3Db/25jRv8Z3DHoDvS6Y1xpFl4vr5P18m2eRlEU5VjuEAwG+fHHH/n0009ZunQpgUAAnU7HyJEjmTJlCuPGjcNobL2vZu/fv5+xY8eyePFiMjIyjn4HEaGlvyq+Y1UeS+ZuI+AL1RzTG7WMmdpbwqJoGY6n66mnDAoywwFyKxTvhOJd6h93JVh7nTUJErpAQjhApvSClP4Q1+GYp7FK19Om1dJ/dwpxLLYUbWFO5hy+3/s93kAVtkBfivIGc26Hc3n12iGYDcfVIuOocspzgJa/PUZz11nuLecvy//C0v1LGZg8kOfOfo50e/rh7xAMQNkeKNoOhVmw9J/gr6x/XWwHuGdzs9QsouNoueeYf1J1Oh1jx45l7NixlJSU8Pnnn7NgwQJ+/vlnli9fTkxMDKtWrWqS4oVoais/y44IiQABX4iVn2VLUBQtw8Arod3p6vtJ3Rt3H0scdBql3uoK+KBoR22ILM6C4mzY+jlsrPMkwGALT2PtCok91RCZ2k9dEynTWIUQjRAIBvg652vmbZvHxqKN6DV6RqWfy45tZ5B1IJ5bz+7Kgxf2lkYrJ0GsOZaXz3uZuVvn8sK6F7h80eX8dfijjLN3Uf8mFO+Ekt1QugvK9kLFAQgFjv7Ah1tHL05ZJ/SSTkJCAjNmzGDGjBnMnj2b5557joqKiqaqTYgm5yqpOuzxBf9aT2yShdgUC/GpNuLTrcQkW9DptCe5SiGaiN4Iaf3VW12KAmX7oGCLGiCLdqghcs8vsHVR7XUavTqqmdAFknpAUi+1kU5KX07wz4cQ4hRR5C7ig+0fsCBrAYWeQhItifx+wO8ZEn8J983bRaGrin9e1p+rhnaMdqmnvoAPSrKhcDua4myuK9nFCH88T7v389Liu8lyubip3IkRQG9Wf78ndoPu4yCha/j3fE945+KGQ2GszLRra07oL/3BgwdZsGABCxcuZP/+/SiKImsVRYtVkluJRhc5E6+aTq+hssxH/q5ygoHa2dhanQZ7nAlHkoXYZDOxKVbi06wkpNuISbSg0coro6IV0mggvqN663Vh5Dl3aThAboXC7eFXnnfB7p8ifng6WpLwx3SGtF7qk4vUPpDST30iISMGQpzyNhZuZE7mHBbvXYw/5GdA0gDuOf0eLuhyAUu3l3DLrA0YdBrevXEYo7rJju1Nxl+lvrBXlBVeZpANpTlQvhcqcoE6s6b0FrrHZfByTB+e1zt5V3eQn9r15R8jH6drh1GH/1099vGG18uPfaw5vzLRAh1zUKyqquLbb7/l008/ZfXq1YRCIQwGA+effz5TpkzhrLPOao46hThuiqKw+cf9rFiQrf5O1IJS9/donTWKiqLgLPZSkltJaV4l5QUeygs9OIu95O4sIxSsDZE6vQZ7gpmYOiEyIc1KfLoNe7xZpte0QLIOrBGs8dD5TPVWV8CnBseCTCjchvfgVgxlu2HLAgi4a68z2CG+k9pIJ6m72kgnpW94Gqvh5H4tos2Sn/Xm4Qv6+GLXF3y47UMySzIxao2M6zSOaX2nMSBpAABvLdvFk19tJSPByqwZQ2Ud8/HwudUgWLRDDYIlu6AsR11H6MwH6rQXMdjUtYOp/aH3REgKd8JO6gkx7UCjwQI8AvTLWsDTq5/mmmX38tCwh5jUY1LDnz+8Lj4Y7nqqka6nbVajg+L69etZsGABX3/9NZWVlSiKQt++fZkyZQoXX3wxcXFxzVimEMfHXe5j8buZ7N1SQkonB+Nu7EthjvOwXU81Gg0xSRZikix0HhD5CqgSUigrdFOa66Ysv5KyAg8VhR7K8t3s31YSGT4NWuyJZmIS1RAZFx6JjE+3Y4szSoiMkvKCgwDEpTZy7Z+opTdC+gD1BhRUN7NJsqlrXAq2QH54GmtJFuxZDls/q72/Rq82zUnoqj6JSe5VOwppjonGVySEaKT8ynzmbp3LZ9mfUeItIcWSwh9O+wNX97qaBEsCAIFgiMc+38L7q/YytHM8b04fQpxV1jgfjsZXCQfW144M1oTBfeDKj7zYaIe4Tur69fjOkNBNncmR3BvsKY2exTG5x2ROSz6N+5fez6MrHuWX3F94bORjWA0NvKAy8EqcRvX3fVzvPif2xYpWq9FB8dprrwUgPj6eadOmMWXKFHr37t1shQlxonZtKOTHuVvxugOccWEnhl7cBZ1OS3yqjZTO6hPTY3m1WaPVqGsXU21AcsS5YCBIeaGX0rxKSvPclBe4KS/0UnLQxd7M4ogX//QmHY4EE7FJFmLC6yHjUi0kpNuxxsgfVdHKaDTqCGJ8J+h1UeQ5dwnkZ0JhprqdR1F4ulT2EiKmR9lSIaFz+FXwHpBSPY21vUxjFSKK1uat5b3M91i6fykBJcCg5EE8OOxBftfxdxFbLri8fm59bx3LdxYz5fT2/HPKAAx6XRQrbyG8FeqLZxENZPaQUbobvack8lpzrDoymDEE4ruFm4v1gOSeahhsIl3juvLBxR/wzJpn+HD7h2wq2sRz5zxHn0QJg6K+RgfFc889l8suu4wxY8ag10sTA9Fy+asCLPswi60rcolJNHPhHwaS3i2uWT+nTq8jId1GQrqt3rlAIEh5voeS3ErK8sMhsshLwV4nOZuKI641mHXEJIans6ZYiEutHYm02GTanmhlrAnQ5Uz1Vpe/Sm3Dnr8lvA5yh7qdx+ZPIFBnTYzRHn71PDyNNaWv2kwnobtMYxWimfgCPhZmL+TDbR+yo2wHZp2Zi7pexLS+0+idUH+AYH+JmxmzV5NdUMl95/fkT+f1iELVUeQurRMGs9UwWLZHnWnhifwbjyUBYjtQ1f50XHEZxKUPVmdXJPYEW+JJK9moM/LIiEcYkT6Cx1c8znVfXcc9Z9zD1D5TZcaTiNDoxPf66683Zx1CNIm83RV8//YWygs99BqRxjlX98Rgju4LG3q9jsT2dhLb11+nEfAFKc13U5pbSWmdEJm3u5zdvxVFXGuy6nEkmIlJUtdDVofIhHQbJqs8aRatiMEE6QPVW12KojZlqA6QhduhZCfk/ARbF9Zep9VDbMc6r7j3UkchU/uByXEyvxIhThkHXQd5b+t7fL7zc8p95bSzteOOwXdwVc+riDXHNniftTkl3DJnLe6qIK9cO5gJA9ud5KpPkspi9cWtovCLWqW7oHSPOk3UWxp5rSUR4jpC17MgvqvaVbR6zaA1HoDC8D6KcVHe73Fcp3H0S+zHfT/dxz/X/JNVuav4++i/H/b/W7Q9MjQoTglKSGHNV7tZ+3UOJrOe8Tf3p/sZTTdVo7nojTqSOzhI7lD/ya3fG6Akz01ZnpvS/ErKC9xUFKlNdXb9GhkizXY9MYmWQ0Kkjfg0K8YoB2UhGk2jUbfiSOgCfS6OPFdZHA6QdbqxFmyDnYuJmNttTw0/OeuuTtlK6Q2pA8CRJtNYhTiEoiisyl3Fe5nv8fOBnwkSZGjaUK7tfS3ndTwPrebw20Mt3LCf//tkEzFmPfNuHcGgDvEnsfJm4CpQp8hX7zdbEwb3QtUhW7/ZUtQ1193GqL+vqoNgUo9Wt+Y63Z7OO+Pf4ZVfX2HW5llctugynjn7GU5PPT3apYkW4JifQW7cuJGff/6Z/Px8fD5fvfMajYYnn3yySYoTojHKC91893YmBbsryOgdz7gZfbHFmaJd1gkzmPWkdo4htXP9Pzo+T4CSvEq1sU6BOqXVWeRl39ZSdq4rjLjWEmPAEZ7OGpeshsiEdDVE6o1taw1Jbpk6rbFTlOsQx8GWCF3PVm91+b1qeCzYqgbH6id5mz+CgLf2OqM93EinKyT2UptApPZVA6VOXkwRbYsn4GFB1gI+3P4hu8p3YdVbubTHpUzvM51u8d2OeF9FUXjx+yxeWpxFrzQHs2YMoV1cK+guqyhqk5jC7epa6ZLsmjWDlO8FnyvyenuqGgZ7jq8dGazeZ9B0anVy1ev03H3G3QxPH85DPz/Ejd/eyG2n3cZVxrNlKmob1+i/joqi8OCDD/L555+jKAoajQZFqX0Vt/pjCYriZMpcfpCfP8oiFFQYfUV3TjuvQ5v4pWa06EnrEktal/rTQ7yVfkpyXeGRSDcVheoWH3s3lbDTWxBxrTXGiCPJTGx4TWR8qrrOMjbNgl4aEYjWwGCGdoPVW12hUJ1prNtq1w/t+gm2LKy9TmtQp4lVbzad3AuSw2shT7Eng0LsrdjLe1vf44vsL3D6nXSwd+DeM+7lip5XYDce/fvdFwhy30e/8flvuYzplcyr156O1dSCXmhRFLSuAijcFX7RaBeUVofB/eCvGwY16kyDuI6QNqHOhvM91BeQjK0g/Daxke1G8snET3hw2YN8tmQx+QdNJFRkEJNQEtEhXrQdjf7pnjt3Lp999hkjRoxg6tSp3HHHHUyePJkzzzyTNWvW8PHHHzN+/Hiuuuqq5qxXCAA8lX5+nLOVXb8Wkdjexu9u6kdiO3lSB2C2GWjXPZ523etPA3I7fZQeVNdDluVXUl7opaLIze6NRfi9tZupowFbnEltrJNsIS7FQlyajYQ0K7HJFnQSIkVLp9WGRw+7AhMjz1UWqQEyP1Ndd1S8Ux2R3Pk9EdNYHWnhkYQekNwj3Eynn3pciFZCURSWHVjG3My5rMxdCcDw9OFc1+c6zs5o/IhRicvH799dw/q9ZVw/qhOPX9wPrTYKL8wqiro2sGhH7chgqdpApmPZPrR1ZxKghZh0dWuJ9qeHZxV0V0cFE7ur66VFhERLIvfF/o1FOb/g0lVQas0jUJbIkrnbACQstjGNDooLFiwgPT2d//73vzVdT9u3b8+ECROYMGEC48eP5/e//z0TJkxotmKFANi7pZjF72zF7fRx2rgOjJzUVYJLI1kdRqy9jLTvFRkiFUXBXeFTm+rkVYZHIr2UF3ko3OMk4K/dykCjBXu8GUdi5EhkfLqVmGQLOt3h17QI0SLYkqDrOeqtLp9bHX0syIycnnZgHQSraq8zxUB8l/BUtJ7qNNaUvurHMo1VtBAun4tPsj5h/o757K3Yi91g54peVzC973Q6xRzbBPysfCc3zl5DbrmHv17Sj+tHdW6eoquFQuqegkU7oCi8x2D1yGDFgchp5RqdurF8XCcqU/rij+9IQvth6shgQjd1D9g2RFEUAlVB/L4QAV+QgD+Iv0p9P+ivPh4i6A8S8IXw+4IE/LUfB/whstbkY/HFotdYcJqKKbcU4vfFsOKznRIU25hG/0XbtWsXl1xyScTWGKFQ7ZPHkSNHctZZZ/HWW29x3nnnNW2VQgABf5AVn+xk048HsMWbuPSuQWT0Toh2WacEjUaDLdaELdZU799UURRcpV5Kc92U5FVSXuBRp7MWecnfVU4wUDsCo9VpsMeZcCRZiE1WG+tUd2aNSbSgicarz0I0ltGqjjq0P6SJQyikPknN3xJeBxneEy17CWz5tPY6nTE8jbWbOlqREg6QKX3AWH/rHCGaw66yXczJnMNXu7/CHXDTOaYzDwx9gMt6XNbwxupH8VNWIX+cux6At64fyjm9mqhRXDCgbiNRtB0Ks8JhMEdtHlOxH4J1+mBo9RCTof58dToz3ECmuzpVPL5LzXY5xeFuoglR7iZaTVEUAj41nAV9Qfw+NbQVl3kI+UME9QX1QlswEAoHtnCAC78fDCgE/aGaUBcMhAj6FQL+IKFAiEBAIRgIEarzN/l46AxaguEXhw2KkXhvKk5TKW5jBfvdXg44D9De0b4p/nlEK3BML33GxtauhzKbzVRURHaB6tatG/PmzTuuQlatWsUrr7zC5s2b0el0DBo0iHvvvZe+ffse9b6ZmZl88803LF++nP379+N2u8nIyGD8+PHccsstWK1tb575qaZon5Pv3t5Caa6b7mckc87U3phlS4iTQqPR4Eiw4Eiw0LFf5D5PiqLgLPZSEh6JLC9Q10M6i9XurKFg7R8snV6DPSG8R2Q4RCakWYlPt2GPN7eJtaWildJqwy3uu0HfSyLPuQrUKawFmbVT4fI3Q9a3kdc50msDZFJPNTym9W/SjbRF26UoCj/s+4H3t77P6rzVaNEyqt0opvWdxsh2I4/79+t7v+Qw8/NM0mLMvDVjKL3SjnH7mYBfDYARG87vCofBAxAK1F6rM9aGwa7nhNcMdoekXurU0RMcra8ObQG/OrrmrwoHM3+QQM37ahALVAVrglrAFwqHt/DH/hCh6uv8ITWk+YNqaAuECPmDxxDa8o94VmfQotNr0Rk06lu9Dr1Bg86gxWQ1ojdo0Rm04WM69WOjFr1Bi96gQ2fQYjBq0Rv16jGTelxv1KI3qtcbTDr1fZMWnU6LRqPhnYeX4ypRZ1Fo0BJTlYg3aKbAvocrFl3B46MeZ3zn8Sf0/yFah0b/1CUnJ1NYWNtNMT09nS1btkRcs2/fvogRx8b68ccf+cMf/oDD4WDy5MkAfPHFF1xzzTXMmTOHgQMHHvH+M2fOZNOmTQwePJhLL70UgOXLl/Paa6+xZMkS3n//fWw2eTW3NVIUhQ3f7WH1ot3o9DrGzehLrxEy7aGl0Gg0xCRZiEmy0HlAUsQ5JaRQVuhWO7PmV1IWHoksy3ezf1sJSu2EBPQGLfZEMzGJaoiMC49ExqfbscUZJUS2MAs3HODJr7ZS6KyiXZyF+8f3YtLgNvoKsz1FvXU7N/J49TTW/C3hlvs71Gms+9dETmM1x0J8Vyy2joTiekCXAZDaX32SrJVp3OLIKqoqmL9jPh/v+JgDrgPEGGOY2mcq0/pMO6FRH0VReOKLTN5ensOgjDj+O+MMkuzmhi8O+NTv7cLt4Q3nq0cG90BFLih1wqDeDLEZKAndCHb5HX5HVwIx3QjYuxCwpOEPB7NAVXhErShE8GAQv/9ATVAL+mtDW8ivjqQFwiHP7fUQDCj8rOQS9IcDXjjMBU90pE2vQafXoTNWhzb1pjdoMVkNNcFMXx3qwkFMb4wMbTqDjtJAMTqDhvZx6eirg1r1tSa9ep1eG7W/fSMv7caSudsI+Gr/UNu1Dk4/fwSZ5d9y39L7WHlwJQ8PexhjG5va29Y0OtX169ePbdu21Xw8YsQI5s2bx+uvv87YsWNZs2YNixcvZujQocdUgM/n4/HHH8dkMjFv3jy6du0KwNSpU7nsssuYOXMmn3766REf49JLL+Vf//oXGRkZNccCgQD33HMP3333HXPnzuWWW245prrE8SkvOAhAXGr3E34sV6mX/72dycGsMtK7xfK7m/riSLCc8OOKk0Oj1ahrF1NtQHLEuWAgSHmhV10PmeemvMBNeaGXkoMu9mYWR/QT0Zt0OBJMxCZZiAmvh4xLtZCQbscaI3+gTraFGw7w0Keb8PjV5kcHyjw89OkmgLYbFhtypGmsJbvUUcfCbWqILNmFYe8StDs/g7Xh63QmiK+extpT3RMytZ+6HrINdmMUkXaU7GDO1jl8k/MN3oCXHnE9eGT4I0zqPgmT/tgatCiKOvrl94UIVAVwunz87fNM1u0uYUqXRG4d2ZmSzYUUlh4kWJpLsKIQxVVCyF1O0OMi5PMSUvQEFT0BDISIw6c9B7/OgR87fo0Nv2LBFzIRDOgJ5gUbCG0HwrfG0ek1aMMhTR1xU9+iBYNFi81sVsOXXh19qw5tapBTR9MM1ceNOvT68GibUYvBqA+PuIVDW/h+TRnacsrVabUdYhOPcmV0VK9DXP7pTtzlPuwJppqupyMC8/n7L3/nk6xP+K3gN54/53m6xneNcsWiuTQ6KJ533nk8+eST7Nu3jw4dOnDLLbfw1Vdf8dJLL/HSSy+pD6bXc/fddx9TAStWrCAvL4+rrrqqJiSCOo114sSJzJ8/n8zMzCNOQZ06dWq9Y3q9nptuuonvvvuOdevWHVNNIvp2rM7jp3k78PuCDL+0K2dc0ElGlU4hOr2OhHR1K45DBQJByvM9lOSq+0OWF7gpL/JQsNdJzqbiiGsNZp3amTXcVCcutXYk0mKLnJq8Y1UemxYU43OH2P71cmn1fRhef4DSSj+lbj9lHh+lbh/lbj8VHj9lHj/vrtxTExKrefxB/rpoC33SY+iSZMUozaUOT6sNT6erfTFtx6o8ln+6E0+5h9gYP8NPy6O74zd1ql7ub7DjmzoPoFEbd8R3qd3TLSW8nYdMYwVq/z0PfYLbGtQNbUFfEH9V7VRJX1WA1QfWsHTvUvaU78WgGBlnv4KhccNJV9oT2Bhi5brdtY1J/NVr2qrXs4XUY4Hwx77q96tDWwgI4Nb66aoJMEAJYs/cw+qt2WipMyqIEWhHUOlACD1odaDVo9EbwWBGZzSjM2jUUTS9FrNBi92oQ6vXhKdEqkGtJrSFp0nqTbo676tBTWfQYTDqMJjC4S58v8M9H8gJr1Hs3ELWKLZmPYenkRLexzkutfbFKYvewj/O/Acj0kfwj1/+wVVfXcWDQx/ksp6XRatU0YwaHRQnTpzIxIm1LcbT09P5+OOPmTVrFnv37qV9+/Zce+219OrV65gKWLtWffl05MiR9c6NHDmS+fPns3bt2katVTxU9TRYnU6etLQWPk+AJe9vY+eaAuLSrFx6Yz+SOx7jmgjRqun1OhLb20lsX3+7k4AvSGm+W+3OWhMiveTtLmf3b0UR15qsehwJZmKSzASDIfZuqZ3u6iqpYvGcrVSUeOjcv3bK7GFfjDjSaxSHu8th73P4B6u5T4lffRuoPMInPszDKeCs8lPu9VPhCag3b4CKKh8ubwCn14/TG8BdFaCiKkilL0BlVRC3L4CrKoAveOTpWRrAVuf96olJVU4/k5//Cb0Wkh1m2sWZyYiz0DHRSudEG10T7aTHmY74gs+RXwxq+Nxx3AXNkf5DD3fqmP+fD3+y7tHstfks+ygr/GRdR1mFju9XdsF/zQX0uCgVjUaDNuBGU92NtWBbeK1XNuxbBSF/7YOZ4yGhs7oOMrGn2kwntZ8aKtvINNYdq/Iipsy5SqpOqK1/IKCuXwuEQ1vQH+4SWd1spCq8Vs0XqFnPFtmEpLrxiFJnvVuwJrSFgrXr26rD3KFChKjSu/HpPIQ0QdIZROfASIwBKzp05BIkl70AaPXqtEe9XoPWoI6UVa9vM5j1WKxBDLgxBCswBEsxBErRB0ow+ItR/GVUoEMhRLKmHLtRQWuPR+9IRBeXijahPfqkThhSu6OLS6lZ2yYv4rZNE7tN5LTk07hv6X3MXDmTX3J/4fGRjzdqP07RepzQyuAOHTrw2GOPnVABe/eqv9w6duxY71z1seprjtXChQsBdZqsaPkObC/l+3cycZVU0e/sdpx5RQ/0Bgn5opbeqCO5g4PkDvVfPPB7A5TkuSnLc1OaX0l5gZuKIrWpjscVqHd9KKCw6rPdrPps98ko/ZgoKChAiIMoEH6fmvfVjxXQ1D8XasRzNgcQq0A71NCiATSKFi2m2o8Bbd3zNR/X/wQhFIJQcwtVKAQOeAhpPBRQQgGwOvwYOgV0gE7RoCX8PqA9YhpvW4IBhR/mbOOHOdsOOZMBmgw0mnFoNBo0GgUNAfQaH3rFh07jQ3fAi17xAX407EPDXvX7RGMkpDUR0prVm86CoreCRqfmWY0GjVbNturzfk34/erjmvA3Qp3j1L1fneOaho6HvwStBu0hj4Mm8v408Hgabf3r6tZVffy3xfsi1lUBBHwhlry/jb3bSgj6akNb0B8iEAiqgS6g1DQlCVV3lwyEIqbBH6vq0KarDm8GXURoM9vDo2b68OjbIU1IigOFrCpewWbnb3hw086Rzjkdz+GsDmdisZgxmLTqCJtRF25YEg5t3opwY6W6DWT2qA1knIWRRZpjIakDe7QZfLg/kSJdMtPPH0H/gWfICLU4qo4xHZl70VyeW/sc7297n81Fm3nmnGcYkDQg2qWJJhL1DZ9cLhcAdnv9VyCqjzmdzmN+3NWrV/PBBx/QqVMnrrzyyhMrUjSrYCDIqs93seF/+7A4DEz448B6jVGEOBqDWU9q5xhSw1Nl6nr1th8Oe79zrwvPgjjME0LlOJ4oKqEQ3kCIyqoAbr86Wuf2hXD7gnj8ATz+IG5fCG8gqN58QbwBBa8/iDcYIlBn66GG6tJqNJj1Wsx6HWajBoteh8mgw6rXYTbosBi06lu9DptRh9mow2bQYTHpseq0DW9TcoSvU6nzj7D5YAVfbszFH6yt0ajTctGAdPq3jwlfX3u/Uo+fQmcVha4qiip9FLh9lHr8lFf5Iv5trXod8RYD8TYjCVYDKXYTyTYTSXYThuPYVuXw/2+H/0IPe5/j+R5o5DfOmi9yDntu8PkdURQFRQk/XvitogCKghIi4ryiAKHwW38lGk8JGk8pGl85Gl8JGp8TTcCLEgSCGvBpCepsBA0xhHQxBHQxKIYYAvoYFK0h8nGrP38oRKjO54Xqz88h16kvdtSrl0O/DmqPo96v5nz1v2Po+H4O6wpUhdi1obAmtOn0uvD0SC0Gsw6zXlcT2qq7R1Y3F6n+WA1ltV0jqxuT6I3VH+vC0yPDoe04vm8DwQBf53zNvG3z2FixEb1Rz5hBY5jW7y4GpwyuvdBdCkVbIS8rvM+guuE8ZXvBEzlFH0sCxHaATiPD+392VbeVSOwJtkRe/3En//xmO12SbMy+cSgdE6T5n2g8g87AQ8MfYmS7kTy6/FGu//p67hh8BzP6zZDR5lNA1INic8jOzubOO+/EZDLx4osvYjYfplOXiLqS3Er+99YWiva76DwwifOm98ZilwYlomnp7HqCDYwq6ux6+p3ZcAMWfyBImccfXq/no9yjrtkrdwco8/gp9/hxetW1e87wlE5XVRCn109lVYCjzODEatBhN+uxm/Q4Ygw4zHpSzAZiLQYcuIg16YhNSiXOaiDWYiTBaiTeaiDOasRqit6v7sGAY0PCCXc9rfIHyS6sJKvASXahi5yiSnKK3awtqaSk1B9xbYrDRMcEKx0TrXRNstEt2U6PFAedk6zoda17SuXWFbk1bejrsieYGDXlxJuC1VPlUrfzKMxUu1QWbVKb65TtVaex+lBvlgR1r7qEbur2BMm91GmsCV2OMt+2eSmKQiik1AbIcDAOhYPnh0+sorLMV+9+9gQT1z85OgoVN06Ru4gPtn/AgqwFFHoKSbQkcnOva7kmYRDJznz4dQGU/ivcTXQfeEsjH8CSGN5W4iyI7xreziW8htUa3+DnDARDPPTRb3y0bj8juybwxrQhOCyy7ZQ4Pud2OJePJn7EAz89wL/W/YvVuat58qwniTc3/P0nWoeoB8XqUcPqkcW6qo85HI1fo7Zv3z5mzJiB1+vlzTffPK61jaL5KYrCpiX7WbkgG7Qazp3ai35nScdE0Tx+MgcY4goBGkIadaqmH4WftR5+e28dFeH1fK4qPy5vAFdVsF7DlkPptWA36bGbDNjNemIsBjLircRY9DjMBuIsBmIstW/jrUbirAbibQbiLMYjB5yinerbpM5N9U/QpCYNbs/ADHVf3a7Jx7cexWTQ0bddDH3b1R8BLvf4ycp3klXgYldhJTlFLvaWuPlmUx7uOv8veq2G9DgzHRPC6yCTbHRPsdMj1U56rKVVvJrdUBt6vVHLyEu7Nc8nNNmh4zD1VlcwoE5TzM9Uu7EWb4fiXbD9K9g0v/Y6vVnd0646iKT0VveETO4LhmPrtnk8NBoNOt3h/19HTe5+cv89T9DGPUv5YPPb/FywHkJBBmotPFxl5JyD2Rgyn4682JYCcR2g25jwhvPhIJjUA8z1f46OpNzj55Z317JqdwlXD+3A3yf1b/UvuojoS7OlMfuC2bz666u8tfEtLv/8cp466ymGpQ87+p1FixT1oFh3HWK/fv0izh1p/WJDDhw4wPTp0ykrK+P1118/5q06xMnhLvex+J1M9maWkNLJwe9u6kdcirR7b82qdqvr/ExdukS5klplbh/LdxaxIruYFQEPB61aBlfpsKGhEoUNpiA52hBFOSXhkT0D7eIsOExqsIu1GIi16MNvDcTZjMRZDOGRPSNWU9tu4uDMV7fCIblnkz92rMXAkM4JDOmcEHFcURTyyr1kFbjYWeBkV1ElOUWV7C1xs2pXCYFQ7TCu1aAjI8FCxwQrXZJsdEmy0SPFQY9UO3HWljNr4Uht6E8qnT4c+npHHlcUcOZB/ia1kU7RDvWFjAPr1RBZQ6t2Y03spjbTSe4V7sbaD6yR/4/Nqebf8+NtuJ2h6Hc9VRRw5YdHb7OgJBtf0U6WOLP5WnGyy6DDEApxqdfLJa5KeprCI4M9B9eODFZ3tzU1TZOQnOJKbpi1mj3Fbh6+qDe3nN0yQ7SIPt/+/eo7qY3/Pa/VaLlj8B0MSxvGw8se5pbvbuH3A3/P7YNuR6uRFyNam6gHxSFDhvDmm2+ycuVKLrzwwohzK1eurLnmaPLz85kxYwYFBQX8+9//ZvToljvFpC3L3lDAj3O3UeUOcMZFnRg6oQs6eRVTNIG6wXBtTgk78l0oqCN/Bq2GHGOIHGNkk4v2cRaWP3hedAoWx0yj0ZAeZyE9zsLZPSP35gwEQ+wpdteEyOqprJsOlPP91oKIa+Ot6uhv50Q1RHZNttEz1UHXZBtmw8n/s3i4NvQtgkYDMenqrcf5keeqnOoIZEF4GmtxlrpWbs9yCNWZ6m1JVLuxJoQDZPU01vjOzTKNtefwNFJi1amZcb37NPnj16MoUHGw9t+geBeUhhvIlO8Hvzo7qkij4UuHnW/sdooMEKu1cWlcHyZ3uYCEtMFqwG7mPTJ/2VXMbXPW4QuGeP26Mzi/X+vYNkS0PsPTh/PxJR/z0LKH+M/G/7Amfw3PnPUMqbbUaJcmjkHUg+KoUaNIS0vj888/Z8aMGTV7KWZnZ7No0SL69u1bM33U7/ezd+9eLBYL7dq1q3mMkpISbrjhBg4cOMBzzz3H2LFjo/K1iMPzewP8NH8H21bkEZNo5qLbB5LeNS7aZYlW7EjBsHd6DDNGd2Z0tyRGdk3kf1vzIzaJB7AYdNw//ti28xG18t254feafkTxeOh1Wrql2OmWYgcin/y6qwJkF7nIyneRHZ7KuqfEzbKsQhZtzK25TgOkxpjpkGBRp7Imh9dDptrplGBDexzNSU5pJgd0HK7e6gr4oaTONNbCHep2Htu/hE0f1l6nt6hhMbGr2lilOkAm9Top01iPiaKoawOLdtSMDFJa3UBmPwQ8dS7WqsE6rhO0P50NZgsfePaxxLkdrxJiUMpg7u1zLb/r+Dv0upP3NOzjtft4aMEmEmxG5v5+OP3ax560zy3apnhzPK+Ne43ZW2bz8oaXuXzR5Twx+gnO7XButEsTjRT1oGg0Gpk5cya33347V199NRdffDEAX3zxBaFQiL/+9a811+bn53PRRRcxbNgw5syZU3P8zjvvJDs7m379+pGdnc3LL78c8Tnat2/PlClTTs4XJOrJ213O929nUl7oodfINM65qicGc9S/9UQrU+b2sTy7iJXZxazJKSUr30lIaTgY2g75/qputPL3zzdR7AkedwMW0TpZTXoGtI9jQPu4eudKKn1k5TvZWeAiu9DF7qJK9pW42bS/HG+gdgTaoNPQPk6dyto5KbweMtVOz1QHyfYj7w/Z5ugN6rrFlENG86pH3vI3105jLd4J+9bCti9rr9PoIKY9JHRVp10m91TXQKb2O2xjliYRCkFZTu302pI6I4MVByDgPaTGdmoYzBgebvrTXa03oRs+YGH2Qj7c9iE7CnZg1pn5XdeLmd5vOr0Teh+ugmahKArPfLud137Mpl+7GN6eMYTUGMtJrUG0XRqNhhv638CQtCE8sPQB7vjhDq7tfS33DbkPg06aJ7V0LeLZ+pgxY5g1axavvvoqCxYsQKPRcPrpp3PPPffUW7fYkIMH1bUyW7ZsYcuWLfXODxs2TIJiFCghhdVf7mbdNzmYzHrG39qf7oNlXybROEcLhtePOnwwbMikwe1Jc5UAMOIs2eNJqBJsRoZ3TWR418SI44qicLDMw/Y8NUDuKnKxp7iSXUWVLN9ZFNHV1m7SkxFvoVM4RHZLUUcie6U6sJvliVANjQZi26u3nuMjz3kroGCLGiALtoWnse6CnJ9BqTON1ZoUnsYaXreX2htS+qnr+qrD+sb5OL55FK07H2IzYOxjMDC8TVYwoI4CFm2HwvDnKM1Ru75W7IdgnY6pWj3EZKiP3enMcAOZ8PTZ+C5qID5EriuXORte5POdn1PuKyfdls4dg+/gqp5XEWs++SN4Vf4gd837lW+25DGuTwr/vnpwVLsmi7ZrQNIAPpr4ETNXzOT9be+zoWADz5/7PB0cHaJdmjiCFvPbYsSIEYwYMeKI12RkZLB9+/Z6x3/44fB7pImTZ8eqPH7+6AAeVxBrbB46gwZnURUZveMZd0NfbLEtbCqRaFGaOhgKcSI0Gg3t4620j7dyXp/IF7j8gSC7i9zhrT0q2V3kYk+xm/V7S/k2Mz/i2kSbkQ7xVjolqeshuyWrTXW6Jtsw6nUn80tq2cwx0HGkeqsr4FdH+AoyoXCrOtJXvBO2fg7+ytrr9FZ1GqveBHmb0FWHy/J9sOA2+Ok5dUSw4kDk+kmdsTYMdj0nPIrZXZ3+GtdJbfJzFIqisCp3Fe9lvsfPB34mSJAhqUO4ts+1jO04NmoNPAqdXm6cvZZNB8q5+ayuPHxRbxn5FlFlN9p57tznGL59OM+seYYrPr+CR0Y8wsXdLo52aeIw5NmWaBI7VuVFtCR3l6uvyvYYlsrvbugrf5xEPRIMjyCpGfbPE03GoNfRM81Bz7T6WzdVegNkFTrZme8iu6iSXYXq1h6LtxbgqqoNKFoNpMdaatZDppsNdE2wMdiQRPv41rG1x0mhN0BaP/VWl6KojWIKtkD+1vC6wSx1GiuhQ64NQnE2dD0buo+rndKa1FMNg9rjC3KegIcFWQv4cPuH7CrfhVVv5dIelzK9z3S6xUe3k+i23ApunL2GAlcVT07uz7XDO0W1HiHquqLXFZyecjr3Lr2Xh35+iF9yf+HREY9i0suAQkvTxp59ieay8rPsiH2rquXuLJMnPAKQYCjaBptZz6AO8QzqUH8tXaHTy458FzsLaqeyVo9E+gK1c1lNei0ZcRY6JtZZD5lip1dqDAn2lrO1R1RpNOqegnEdoOcFtcdnxjV8vRKEaQua5FPvc+5jTuYcvsj+AqffSQd7B+49416u6HkFdmPTbGFxIhZvzeeueb+i08DsG4ZyZvfko99JiJOsW3w35l88nydXP8mnWZ+yqWgTz579LD0TWkaDNKGSZ2OiSbhKqo7puDj1STAUIlKyw0yyw8zo7kkRx0MhhS1ZxewqriTXH2BXYSV7iivZnudk6Y5C6mwPSYxZT4cEa01TnW5JNnqk2emR7JC1Z6CuSSzf1/DxE6AoCssOLOP9re+z8uBKFBSGpw/nuj7XcXbG2S3mBdFZP+/m719m0j7ewqwZw8JdgIVomUx6E38d9VdGpo/kbyv/xrVfXcv9Q+7nqt5XRbs0ESZ/VUSTsCeYGgyF9gSZRtBWlHsDrNl0UIKhEMdIq9XQIc5KhzhrvX0Uq/xBdhVVkpVfvR5SDZGrdhXz9ea8iGtTHCZ1PWT1/pApNnqmOOicaMXQROshc8pzAOgc27lJHq/JjX0MFt0J/jrbVRgs6vHjUOmv5JMdn/Dhjg/ZW7EXu8HO5b0uZ1rfaXSO6dw0NTeBUEjhsc83894vexnSKZ43rx9CvFVGn0XrcEGXC+iX1I/7l97P31f9nV9yf+GJ0U+0iBH6tk6erYkmMfLSbhFrFAH0Ri0jL43uOg3RfMo9fn7eWcjK7GJW78hjZ4mPEBIMhWhKJoOOPukx9EmPqXeu3OMnK99JVoGLXeH9IfeWuPlmcx7uOnuG6rWQXr21R2LtVNYeqXbSY0+x9ZDh7qbBcNdTzaFdTxtpV9ku5mTO4avdX+EOuOkc05kHhj7AlB5TsBlszVH5cav0BvjD3HX8lFXEpEHtePbygU32woBo22ITTl7X5g6ODrx34Xu8sP4F3s18l8ziTJ4951kGJg88aTWI+uTZm2gSPYerG1z//NF2PK4g9gQTIy/tVnNctH51g+GhI4a9Ekxc1z+es0/vJsFQiJMk1mJgSOcEhnROiDiuKAr54fWQ2eGmOnuKXewt8bBqdwmBOnt7WAw6MuJr94dUu7La6Z7qaL0jUgOvxGlUt8CJ693nKBfXUhSFH/b9wPtb32d13mq0aBnVbhTX9b2OUe1GNUugrtq9GwBTly7Hdf8DpW5mzFpDVoGLe3/XkzvO635qBX/Rpuh1eu4fej/D04fz6M+Pcv3X1/PHwX/kpv43yfd1lMizOdFkeg5Pw2R1AdBpgHRtbO2OFAwPHTHU56prgkxdUqNc9eFtXbaEte+8RZWzjI3zkjnr6un0OWtMtMsSoslpNBrSYiykxVg4u0dkI5NAMMTeEjdZ+S52FrrIKaokp7iSzQfLWbytIOLaOIuBDglWdX/IZDVE2qxuOiWeWksKKqoqmL9jPh/v+JgDrgPEGGO4tve1XNf3umbf421fiRuA7seRE9ftKeHWOetweQO8fM0gJp7WvomrEyI6zs44m48u+Yj/++n/eGn9S6zOXc3TZz1NgiXh6HcWTUqCohACOLZgeOiIYUtvWbR12RK+e+MVAj61UmdRId+98QqAhMVT3ImO2Jxq9DotXZPtdE22c8iW97irAuwuqmRHgZOdBepU1j0lbn7eWcgXm3JrrtMAKTG7a6eyJtvolqxOZe2UYEOrbR2v/O8s3ck7me/wbc63eAIeesT14JHhj3BJ90uw6C3RLu+IPv/1AA98vBG7Wc8Ht4xgcMf6XXaFOFGlheqLR2lR+P2ZYk3hrfFv8fpvr/PGxje4bNFlPHXmU4xod+Q910XTkqAo2qRDG0a0RScSDKNBURT8Xg8elxOvy4XH6aSq0om30kWVq5Iqd/hWWYnXU4nPXYnP48HndlNRVFDv8QK+Kr578xVKDh4gJiWVuNRUYlPTcSQkyRQX0SZZTXr6tY+lX/vYeudKKn1k5TtZtWcP+4qrKHHp2VNSyab95XgDddam6zTq1h4JVjrV2dqjZ6qdFIc56j9bISXE//b8j/e3vc/6/PXoNDrOzjib6/pcx7D0YVGtrTEUReGlxVm8+H0WPVPtzJ4xlHbx8vdMnJq0Gi23D7qdYWnDePCnB7n1f7dy44AbuWPwHWg1x7f/qTg20X/2J4Q4KaIdDBVFwedx43E68boq8Lhc+NyVeF1OvJWVVFW6aoJeldtNlceNz+3G73VT5fHg83hQQsEjfg6dXo/RYsFgtmKyWDBZrTgSkxoMigCBqip++XRevcewxSdiT0ggJjGFmOQUYpKTiU1JJy4tHUdSEjqd/OoUbUuCzcjwromkJjqB2q6niqKQW+Zhe76L7EIXu4oqySlSu7OuyC6iTobEZtSRUT2VNbwesluynV6pDhyWpm2aEZcU+Xhl3jLmbZ/HJzs+Ic+dR7wpnuv7Xc91fa4jzdY61tL7AkEe+HgjC389yNk9knht6hkt4kU8cexabNfgFmpI2hA+ueQT/vLzX/jvpv+yJm8Nz53zXKv52W3N5DeMEKeopg6GiqLgrawOdk68TidVbhdelwvXvn34vB4Cej1VnkqqKt34PLU3Nei5QVGO+Dl0eoMa9CxWTFYLFoed2NRUTBYbJpsNk9WKyWrHbLdjttkw2x2Y7A7MdjsWmwOD2dzg477xxxtwFhXWO+5ISmbaP1+mLP8g5fm5lOfnU15YgLOoEFdJEbv3r6Oq0hVxH41Why0+Dnt8Eo7EJGKSUohNTSU2OZW41DRiUtLQG05epzghokmj0dAu3kq7eCtjeqdEnPMHguQUu9lR4GRXQSW7ilzsKXazYW8p32XmR1ybYDOEt/aw0aW6qU6qg27JNown0MFza/FW3s18l//t+R9VwSp6x/fmttNuY2LXiRj1radZT0mlj1veXcvaPaVcN6ITf7ukX6uZ4nsySQA7dcWaY3l57MvMyZzDS+tf4rLPL+Nvo/7G2E5jo13aKU2CohCniHKPnxU7i1ieXczanBJ2HBoMR3ZkWHsrA5MMaHxuvC4XVcVb2bHPRZXLhbeyEp+nUn1bZ0TP561+6wWOHPT0BiMGiwWjxYrJasUaE0tsapoa7mxqyDPZbZisDsx2GxabHbPDgdkeg9luR29oniduZ109PWKNIoDeaOKsq6djsdux2HuS3q1ng/f1edyU5uVRXqAGyYrCfCqKC3EWF7I/cxMeZ8Uh99BgjY3DnpCIIylJHZVMSSE2JZX4tHRiU9thMLaeJ6hCHC+DXkePVAc9Uh31zlV6A2QVOtkZ7sq6u7CSvSWVLNlewOe/BWqu02ogPdZChwQLncMhsnuynR5pdjLirA2GpUAwwDf7l/LBnq/ZWLYdvUbPmA5jmNZvGoNTBjfr19wcdua7uOGd1Rws9fD4xL7cMFrW24q2SaPRML3fdM5IPYMHfnqAu3+8m6t6XcUDQx/AqJO/q81BgqIQrUQwGKCq0o3XWY7H5aK4uIxNu/PYfqCE/YVllDvd6EI+zCE/fQxBRml9OPBgCngJHHDj/9lLJpB5hM+hN5kwmC2YwmHPFhdHvK09JosNs82GyWrDZFPDntlqx2x3YHbY0ZaUYrLYsPXocbL+OY5JdcOapXNnUVlagiOp8V1PjRYrqV26ktqla4Pn/T4f5QW5lOXlUZ6fR0VRARVFBTiLisjbuYPstavqjaRaHA7sCYnYE5KISU5VRyVTUohLa0d8ajpGq6w5Eqc2m1nPoA7xDOpQvwlLodNLVoGLnfnqVNY9xZXsKXazfm8pvkDtz5JJr6V9nIVOiepIZGpsgJzA96wq/IriqgISjLH8fsDvuab3NaRYU+p9ntbg552F3P7eekIKvDl9KOf1aZ1fhxBNqV9SP+ZPnM9fV/6VD7d/yIaCDTx/7vN0jukc7dJOORIUhThJgoGAOm0z3IxFbcLiwlPpDK/Lq16f51LX6B2yPs9X5SWIHr9WvQXr/Ph2IECXUACjXofDYsRssmC02DBZkzFarWrAq77ZbFhsDkx2O2ZbddhzYLbZ0emP71dClT909IuirM9ZY0gNjxomtGu6NvIGo5GkjE4kZXRq8HwwEKCisICy/FzK8nOpKKwNkkV7c8j5bUO9tZcmq602SCYlE5OcQmxKGrGpqcSltcPqqL/5umjdpMFWrWSHmWSHmVHdkiKOh0IK+8vcZOWpW3vsDm/tsaVoKytKf0Fr3YlGEyRYlYbBcyEW8+lsDcQyq6CEbklVdE+10yPZ0WrW9b2/ag+Pfb6FFLuJt2cMpXe6/NwLUc1msPHM2c8wIn0ET69+mqsWXcXDwx/m0u6XRru0U0rr+G0pRAsQ8PvrrM9zhd8PB77KOiHP48ZXWam+Da/P83s8EdMeD8dgNmO0WDFarGhNZirNCRTrDeSb9BT4tHg1RoJ6I8lxDrq1S2Rg11SG9kwnMTERk80qTVZaIJ1eT3x6O+LT2zV4XgmFqCgupDQ3l/LCPCoKCqgoysdZXERp7gH2Z24iGPBH3MdgNmOPT8CRmIwjMVkNk6mpxIUb7tji4pu9u2R6XMPrQYVoLlqtho4JNjom2DgrGM+Xu75k97Z5eEyZWDVGhiWfy2kxF1Plbs/u8Ejk6pxivtmSF/E4KQ5TeD2klS5JNrqm2OiZ4qBzohXDCayHbCqKovDkV1t5c9luBrSP5e0ZQ0h2yM+bEA2Z0mMKg5IHcd/S+3hk+SP8kvsLj454FKtBXnxrCvKsUrQZAb8Pr9OJ2+mkqrIiPKoX7rYZDnneSjdVbpfahCVinZ6HoN9/lM+gwWg2Y7RaMZqtGK1WYpPTMFqt4WmbdrUhi02dtmmyq01ZLPYYzA4HVRojK3eV1F9jaITeHWMY0jmBUd0SGdUtEbtZmqWcKjRaLbHJajOchiiKQmVZKWW5BygryKe8IJ+KwgKcxYVUFBdyMGsbgarIFyF0BgO2+ERiEhPVIJmsdm+NS00nNi2dmIQkNFppLS5an/zKfN7f9j4Ldy6kxFtCiiWFP5z2B67udfVhN+N2evxsz3eSXegiu1DtyrqnxM03W/Jw+2pH8/Xa6vWQalfWrknVXVntpMdZTsrWHu6qAHfO28D3Wwu4sH8aL141CJMh+uFVtD3lixZR/vQ/CRUX42zXjpR77iZ24sRol9WgrnFdmTdhHv9c80/m75jPpqJNPHf2c/RO7B3t0lo9CYqiyWxdtoQf57yNu7z0mNaANZbf68VT6VT3z3M5qap0hbtwutSRvPD0Ta+7Mhz0Kqlye/B71ambh47K1KPRqE1YwuvzjBYr8bFx6tTNmq6bdsx2G2a7XX3f4cBsc2BxODBZbcf0RKKm+cyWYtbm7IxsPpMWw/SRnSUYCjQaDfb4BOzxCWT0HVDvvKIoeJxOyvIOUp6fR3lBeJ1kYSHOkiIK9uTgc1dG3Eer02GPT8CWoHZujU1KJiYlldiUNOLS0olJSjniNOSty5bw05z/4i0vZ2Uz/KwLcaj1+euZkzmHH/f9SEAJMCh5EA8Oe5Dfdfwd+qPMpHBYDAzpnMCQzpFBUlEUCpxeduS72FkQXg9ZVMneEjerc0oIBGvXQ5r1WjISLHRKsNEpyUa36v0h0xzEW5umiUZehYebZq9ly8EKbh/TjfvP7xX1fSdF21S+aBG5jz6G4vUCEDh4kNxHHwNosWHRqDfy6MhHGZ4+nJkrZnLd19dxz+n3cG2fa+Xn6ARIUBRNYuuyJRFdJZ1FhXz3xiuAujasZrP0Shdep0vdKN3pxOMOb5ZeZw89r6cSf82Inrqtgs/rIRQIHKkENFqtGvDMZowWG0arFVtcorqlQp1GLNXbKhitNix2O2Z7DBaHA6PF2qy/TI7YlVSCoThOGo0Ga0wM1pgY2vVs+NXTKnclpdVBMj+fiqICyosKqCwuYu+mX/G6nIc+KLa4eOwJicQkJdeMSsalplO8fy8rPnqfoN8H1P9ZF6Kp+AI+Psv+jHnb57GjdAdmnZkLu1zI9H7T6Z1w4iMFGo2G1BgLqTEWzuqRHHEuEAyxr9RNVr6LrAIXOeH1kJsPlrN4W+S+rHEWfc3+kF2S7XRLVqeydkuxYTYc/mnWwg0H+MeiPRR5FZK+PkhVIITHH+S5ywdy+ZAOJ/z1ibYt5PMRLC8n5HQSdDoJVVSob8O3oMtF0OlCcbkIuSsJOl2EKisJuivx786BUGTvAcXrpeCFF1tsUKx2fufz6Z/Un/uW3sfTa57ml9xf+MeZ/yDGJGt8j4dGUY6ysVkbs3//fsaOHcvixYvJyMiIdjmtxuH2qdNotBit1kZtlq7R6jBZrRjNFnX6psWC0WrDXDOaF77ZbZhtdkzhkTx1+qYDg/nkTA1qrKMFw1NpKmnV7t0AmLq07LbtJQcPAE3bzOZU4Pd6Kc3Pozz/IOWF+VQU5FNRVIizuAhnSTGe8rKjPobeZOK0cRdgtoWbI9nDo+0x6tRqqz3msPtcNqfW8r3ZWuSU5wDNu19driuX97a+x+fZn1NWVUa6LZ3Le17OlT2vJM4c12yft7HcVQF2F1Wyo8CpTmUtVKey7itxU+aJnLmSGmOiY4KVzok2Oldv7ZFqZ8OeUh75bAsef+TfxTvO68afz5fpcm1ZKBBQw1x5OYEKJyFX3ZDnIuRSQ17IVale564k6KokVFlJyO2uueHzHf2TabVorVa0NhsaqxWdzYbWasW9enXD12s09Nl6pN7pLUcgGODfG/7N7C2zSbWm8s9z/snpKadHu6wW52i5R0YURZNwFhc1eFxRQnQZfIa6Ps8a3mIhPKJnttlrN0u3OzCYzC0q6B0rGTEUrZXBbCalU2dSOnVu8HzA76M8P5+y/DwWPvPXhq+pqmLdl58d8fPo9Hp11L+6E69FHe032xzqaL+9+tYyQqY4eRRFYVXuKt7b+h4/7/+ZIEGGpA7h2j7XMrbjWLSalrOm1mrS0699LP3ax9Y7V+r2sTPfSVZB7XrIvSVuFu0/iDdw9O7Qn64/KEGxlVJCoYiRO3U0LxzsKioIuSrDAc+phjxX7QheqNKNEg541dM9j0ijQWO1orVa0FptaG02dPFxGDMy0NpsaO12tA47OpsdrcOhvu9woI2JReuwo4+JQRsbi9bS8AvsWeeNJXDwYL3j+vT0pvinOin0Oj33DrmX4enDefjnh7nxmxv5w2l/4JaBt7Tq55onmwRF0SQciUkNjig6kpKZcMf9Uaio+UkwVJUvWkTBs88RKCxEn57eohe8i+OjNxhJzOhAYkYHHEnJh/1Zv+mlN/G6nHhcFXgrKvBUbwVTsyWMM9xASl1P7CorpST3AFVuN4GqIz85qhcyrXUaQx0hZOqqqjCYTM31TyNOkCfgYUHWAj7c/iG7yndh0Vu4pPslXN/3errFd4t2eccs3mpkaJdEhnZJjDiuKAq55R6ywushn/hya4P3P1jmORllijoURVEDW0WFGugqKtSpmRVONcw5K2pH8FxOgpVqsAu5XLUjeJWVKJ7G/d9pzGa0Visamw2d1YrO7sCQmobWpr6vtdvR2m1qsLM70MY4wiEvBl1MLLoY9ZrmDDsp99wdsUaxuu6Ue+5uts/ZXEa3H80nEz/hwZ8f5JVfX2F13mr+edY/SbImHf3OQoKiaBpnXT09Yo0igN5o4qyrp0exqqYlwbC+1rjgXZyYI/2s6/R6bHHx2OLqb6J+NNX7jNYNmR5nBd7wGuZ6IbO0hJKD+48xZNrUNcuNCJmWmBgsjlgMxqZpVCIi7XPuY07mHL7I/gKn30kHewfuOf0erux1JXajPdrlNTmNRkO7OCvt4qyc0yuFt5fncKCBUNguzhKF6lonRVEIeb2EyssJVVQQcLoIVZRHhLxgpUudnhmemhmsdKkhr7ISpbKSoMeD4nbXW4/XIJMJndWqjuTZbOhsVvSJiTXBTmu3h4OerSbYae0OdDEOdDEx4aAX0yo6Tlf//c4Ldz3Vt/Cup0eTZE3izd+9yZub3uS1X1/jskWX8Y/R/+DMjDOjXVqLJ0FRNInqJhbN2fX0ZJNgeHT5zz5Xb5qM4vWS+9e/ESgqQmuzq9NgrJaa6TC68Cup2pgYtEajTAFpZap/pn8Idz1tqp/1Ew2ZaqisUN86neHRzEoqD+5T9zLVaiNCZvGBffg87npbi9Svy1CzXvpIIdPiiAmvm47BEuOQkNkARVFYdmAZ7299n5UHV6KgMDx9OFP7TOWcjHPa1O+C+8f34qFPN0WsUbQYdNw/vlcUqzp5QlVVNaN3NSN5znC4C6/FC7pcKJUudf1dePQuGA54IbebkMcDR2lyB4DBoK7Ds1rUv0lWK4Z26eq0TJsNrcMeDnl2tI4YdZpmTAxahwOdIwZdrPq+1tC2/s7HTpyIJzUFgLRhw6NczYnTaDTcMvAWhqYN5YGlD3D74tuZ3m86dw+++6idk9sy+ZcRTabPWWOwxqmd2joN6B7lao6dBMMjC/l8eDZuwrNmDZ7ffsWzeQvBosOsTXW5KPjnM0d/UJ0OjcmE1mxGY7GgNZvVNRNWC1qzBa3Fok7RCf+R11mttR/bbOG1GVb1j3z1ugybDa1MNWxWu9q7+XjMfop8JaTbfLRv76ZPFOvR6fU1W4gc6mjNbI4UMmtHMsPdmd2VuEqKmyRkmu3hdZkOdY32qRwyK/2VfLLjE+bvmM+eij3YDXYu73U50/pOo3NM52iXFxWTBqsNtf7x2W8UeRXaxVm4f3yvmuMtVcjvV0fsyisIOisim6zUTNF0qR9Xj+C5wsGu0kXI7VEbrRx1X2JApwsHvPA0TZsNQ3Iy2i5d0NltaGzV4S68/s4Ro47mhUfuqkfw5O+BqGtwymA+ueQTHln+CO9seYf1+et57pznaGdvF+3SWiQJiqLNcnr8LM8uZnl2EWtzStieJ8GwLn9hIe41a3CvW49340a827fXdFHTJSdh6d+fyrVrUVyuevfVpabS+b056loOV2X4CYK79pXgSnVdR/W0H/XVYTeKx0vI4yFYUorfm4vi8RDyetVRy+CRu+ZG0OvVdSAmU0QADeq0aMxm3PHxaC1q4NRZrWogtdV2ftNarerakPD0opoAego9eT9eX+76kpkrZuINqiPJuZW5zFwxE4AJXSdEsbLjc6SQeTSHDZnOOlNl3U0TMtVOz+G9XG32VhEyd5Xt4r2t7/HVrq+oDFTSOaYzDwx9gCk9pmAz2KJdXtSN2b+ent8+DaUlGNq1I6Xf3dBMQVEJhWpG8AJ1mqzUBD1XJUGXMyLk1XTTdKuNVoJuNxzl+xZQG63YbOgsFvX3qd2OLiEeY4cO4ZklNnW0zhYeyYtxqCN2MbHoHXZ1tklsrPoCYhsaZRYnT4wphpfGvMS8bfN4ft3zXP755Tw+6nHGdx4f7dJaHAmKos04XDDUaaBPetsOhkoohHfbNtyrV+PZ8CuezZsJHFC3kkCnw9itG7GXTMR6+ulYhw7F2EEdOT50jSKoC95T7/tzzTVNJVRVpU5Nqqwk6A43EnBVEvKo602qmwkEw0FUCU9NUm/hEFpahuL1EtyRVRtAG7M2pZpeHx79NKMx1wbQmps1/MTIEu5EVzPyGf7Ybqtd32J31ITT1jSl6aX1L9WExGreoJeHlj3Ev9b+C6POiFFnxKQz1b7VGjHrzTXHqt+vvpn1Zsy68M2gnjPrzJj1Zqx6Kya9CYvOgsVgwaK3tJgOmE0RMt0VFVS5wmsyXU6qDhMyncVFFO3bq4ZMXyNCptWqbjdkqe02fTJCpqIoLNm3hPe3vc+q3FVo0TKq3Siu63sdo9qNkif+YdW/OzVHWd+tKIo6HbNOo5VgRXgvPJezJuSFnM7a9Xc13TRrX5xrVCdNUF9Yq/m9ZUUXGxuepmlTXzyz22qnaEaEvJjakTybTf6fRYun0Wi4ps81DE4dzH1L7+O+pffxy8FfeGjYQxj1LePFtpZAgqI4ZUkwPLyg04l77Vrc69bh+e03vJlbUSorAdA6HJj79iV24sVYhwzBMngwOlvDr/5XP6E5GV1PtSaTOoUo6fg7lR26j2JNMwSXS21sUN3FrrJ6TypPzWhoyOMJj4qGR0C93pog6i8qRHF7ULze2gB6LFvUGgxq6AxPwdVYzBFTb7U1XfLCo52W2pboNWEz3EFPY7ejtdnROezN0jQhrzKvweMKCgOSBuANevGH/HiDXqoCVTh9TqqCVfiCPvUWUt8GlWMYIT6EXqOvCaRGrRpGDTpDRDitvum9fowaI7aCpJoAatKH31YH0nA4tegsmA1mLDpLs4fTEwmZAb8fr8vZiJDpwuuuxFlc2PiQaTBgtBw+ZLr1Pkw2G1XJ+9R1mXZ1P9uAScuC3Qv5eMfH7HftJ8YYw7W9r+W6vtfRwdH6No9XFAXF71dvPl+d9/2EfFUoPh8EAuq56rf+QPg69Xqq7xMIoPjCb8PHSj/8sMH13QcfepjCl18JvwDmVjtpNuZ3idlc22glvB+ePjm5ttGKLTw9s7rRSvVavNjY2o6aDkeraLQiTo6ty5bw4zv/xe1surXoLVHvhN58NPEjnlj5BB9nfcxvhb/x3DnP0TWua7RLaxEkKIpThgTDhimKgi9njzpauH497s2b8O/aXfPkw9CxI45zzsFy+ulYhg7B3LPnMb0aHDtxIub+/YHWt6m5RqNBZ7Ggs1ggObnJHldRFDVEutRX+IMuZ51pt5HTb0Pu8Cv+Hk/N+p3qEOqvqEDxhqfferyNHhWoYTTWrvs0mWpHPc2WOoHTisZiUafYht9Xg6e9JoTWXf+ZbknloKd+WEy3pfPieS82urRAMIA36MUdcFMVrMLj9+ANevEEPHgDXrxBL96Al6pgVc3HVcEqqgJVeINefEGf+jYQfhvyqecC3ohwWuX34Av58ecHCNK04bTuyOmhAbU6jBp1xohwWvO+PjKcWnVWdWQ1fDtSONUbDM0TMl0VeCsr8Va68LkrjxgyN4TfBjUKPkOQKr1CQBfidJ2W8wx9SLWnYtnpYv3P75JptmCyWDCZLJhMZkwmEyajGbPBhMloxGgwoI8IZn6UgL82XPl86sd+vxrO/H5Cfn9NMKs+VntNsDag+f0owfDHwUDNcfwB9W3NuaB6fTCoNkhpTJOU5hAIYOyQEZ6BEF5/F37xp+42CVq7A32MQ52i6XCg1cvTOdF0ti5bEtHd2llUyHdvvAJwSoZFi97Ck2c9ycj0kfx91d+5+sureXDog0zpOSXapUWd/GYRrZYEw4YFq6rwbtiAe+06PL/+infLFoKlpYA6LdTUuxf26dOwnnEG1qFD0ccfe5dJcWQajUbdH8tqbdLHVRRFDZLhhhHBynCDiMrK2rWe1Xt8hUdAQ+5KdaTT7VaDqDc8BTe8/jNUVQXHEEBfBKr06s1ngCoD+A0aUhN07P36JjVsms21U2/rrP+MaERks2OwWYm329HaE9DGW5t8ulr5okUUvPIcgcJy9OnpJNx1B6YLx+EOuPEGakOpJ+BRA2k4nNYE1EPCaXVo9QV9ahANh1NPwEO5r5yqQFXNiKkv5MMf8DdJODXpTBg1BqwhPWaNAYtixKoYMKHHrOgxhXRY0GNS9JgVHaaQHpOixRjUYQxpMSpajCGN+n4Q9CEt+qCCMaBBH4KYIOiCCvqgUi9kEdSiBI0UVpWyp3Q3Sgg0IS0+nRaPSYtfrwG3FnPIgMVvQKto8WsKCFCAW6vBqdEQ0GkJHWVEVquE0AdD6EIKulAIfUhBHwqiDyjogyH0oRCGYBB9SFHfBkMYgiEMGg06rQ6NXh9xQ6dDYzCo65Xr3LQ2k/q+Qa+e16lv1WOG2vsYDDXX1JzTG9AYjZHHjcY61xvV80b1cbThx6u5xmiqOZd9/viGNzRv146Ob7113N8zQkD4b0UwSDDgJxgIEPT7CQb8hAJBggEfwUCQoN9PKOAn4A8QCgbU80H1+JJ33qw3+yDgq2LZvHdPyaBYbWL3iQxMGciff/wzj698nJW5K5k5amabXlMtQVG0GhIMG+bPy8O9ejXu9evxbNxE1Y4dNa+G61NT1ZHCQadhHToUc79+rWo9nIik0WjQhUf4SE1tssdVQqHa6bYuV+2G0pWVtUE0HDyDlW5y8rez/cAG8FYREzLRyZhGXMiEb99+lKoqNaR6vTXNjxr99YXXf2rN4TWg1Ws/w1NvtVZL7RqqmjWg1og1VZrw+k/XihUUPPNMTfONwMGDFD7+V1JDkDj+fJSQESVgVkelfD7w+wjVnUYYCKBUhd/WnUpYb5qhv/aa6nDlrx25CviqCAR8BH1VBII+gj4foWD4SZvfTygYIBQIQChAKBAkFApCMARKEE3AD4oPJVSBBlAARRO+HeF9GsjbIcAbvjUkqIGgFhQthDSgaDUoGi1oNShaDT7FT1AHIQNUGUL4ws8ezCEdo5KHYTPZ0Rj0taFJb4BwmNIaDAS1GnwaBV8wSJWi4AsE8Sl+/IEgVUE/vkCAKn8Vfn8An6+KSl8Vfp8Pn89LwB9o+OsKv7BQd7qsyWqv7S5rs6nrMW21+2TWnS5rjYlFb4jOWqRTaUPzU5kSChEIBMKBSv25DfrVn9lAIPzz6w/UBrJAgJDfT7A6pIV/vtXj1YEsEA5xvvDbQDjAhc8H1fOh6uuCteeVUIBg+PeE+vtC/Vipvj4UUms6luZvx8BZ3HCn81NJp5hOvD/hfZ5b+xwfbPuALUVbeOacZ+if1D/apUWFBEXRYkkwrC8UCODNzFS3qPj1VzybNhPIC08D1Osxde9O3JQpWE4fjHXYMIztpN2zODqNVovOoU5ra4w0wLZjFQADeh5+f61QIBAOnOE1oNVhtG4QdVdGrv+sbkAUXhcadDkJFBbWjoB6vY1rrd8Axesl76GHyHvooeO6f6PUHdUy6NHoaj821h3dMtjR2HRqmKo7qlVz3qDe/9ARrZqRLEP9US2jEY3eQECnwa9T8OtCVBGiSh/ERwivLkSVJkiVLogHP15NEK/Wj1fx4w1V1UznrQpUqSOp4dFU3/9v787joiz3/oF/ZoVhkS13VFyaUXBjCRDSXLNyOVqZmksn06eOHUXLwjyZthyzo/b7PalHj5FmZZqUeNQkPGW2qcgoiLigQmpHFnFBgWGZGe7nj4HRWxYFZrgH+Lxfr17AdV9zc13fkIvP3Ft5GfS5+mqnK4MMzz+30X71BGAylqGkoACGglsoqby7bFEBSgoLUVJYUPEYkyLrczJvXbuC0j8MMBoMMBlrf7NCqVJDVXHjHyeN6z1DprN7K7hUfFQ24E03jzFjcP73czj8039QopBDUy4gbNDwJvtA89oIglARhowVoctkOaplNMNkNFreODGaUG6uOLplMqPcVAaz2WwJYebbQczyWjPKTZYwVm66M3yZbocuc+XXZgh3hC9zxenFZpO5IpRVbq8IW2az9XOz2VS3a83rQSaXQy5XQK5UWD4qFJArlVAoFJApFJArLJ/LlUrLdrUGzgol5AolZEoFFBXtCpUScrnS8rWy4qOiYrtCefujqnK7CgqFAgqVqqKfCnKFEkqVEnKlCv9e9XcY8m9UGa+7T/3vEdCUqBVqLApbhLD2YXjrt7cwPX465gXNwzT/aS3uRk0MiuQwaguGPdu3wrQBXRDZ/YEWFQxN+fkwJFluOlNy/DhKTp+2vgMt9/SEJsDfEgxDQqAJ7G+51o7IQciVSsg9WgEerWy633KjseIUXMst/YXKU24LK4KnoRi5771X4+t9XnpRHLxUKkvwsgY0VdVTDdXq26cTqtW3+6jVt7c14+vEHv36UWQXZVdpb+fazu7fW6lSw83bB27ePnV+7d0h0/I4k4qQWVRo/5Dp7g6Nqzuc3d1FIfPc4V/xa9JvMCkVAIBihQy/Jv0Gl18Cazy1Tygvh6mszBKcjEbrx3KTqeL0wcrAZbIEMJPJerqhyVSxvTJQGU3W0xCtR7XMt49qlZtMFUGsMpCZK45UVRO47jgabjnqVQ6zyQjBXG4JXeX2Obp1J2uQUlT+p4RcKYdcroRcqYBCoYRcIYdcoYJCpYZaU9FHUbG98vUVAcraplDdDl8Ky3brtopAplCoLAGsIpApKoKYXFUZyJSWQKZUQKlygkJpCWgKhdJhbx40eOoM0TWKAKBUO2HgpOkSjqrxDes8DAE+AXjtp9ewQr8Ch7MPY9nDy+Dp7FnnfV24eQEA4OfhZ9Mx2lvzXdXI4TEYigmCgLLz51FU+YiKEydgvHjRslEmg8rPD+6Pjrj9iIpu3VrcO1tEACBXqSD38AA8PGrsc23jxhqvAWszb54dR9c8RQVFiZ6fCQDOCmdEBUVJOKp7a2jINNy6ieKCApQWFtw7ZF7NReklA8qKDTDX46i3qawUe9d+iANfbKw4ymU5jbAykFWcWGw3MrlcHLTk8juObt15hKpiu1ptPbolVyrER7IUqtv9lbePit0+snXndpU1YFn6WtqUSgXkKpUlbKksgUyuUlYEL7UocJFtVb5Z0RLuenov7VzbYfPjm7E6eTU2ntiIp3Y9heUDl+Oh9g9JPbRGwX9d1GgYDMXMBgOKk5Nh0OtRnHIcJSdPovzWLQCWZ1k5+/vDfdhQuAQHQxMSAmUtfxQTkRivAbOtUd1GAQBW6VfhavFVtHNth6igKGt7c6RUqdHKpzVa+dT9jsj3Cpn63Tuqf6EgoKOul/VolyVYyaFQqsXB685TCu84wqVUqSraLUHM+rWqMnxVfFSpbn9dceSLbzzSnXoNHAIvJ8sN2dqF1nyJQUsgl8kRFRSFsHZhWPTrIszaNwv/0+9/8FK/lxzmub72wqBIdnN3MDybUwBzCw6GZZezYEhMtDyi4kQqyjIyb990pn17uIaFQRMYCM1DIdAEBDjsKSlETUFjPuOzpRjVbRQCfAIANL3TpxrbvUJm+qFfUHA1r0q7+wOtMfaVRfYeHhHVQ3iHcHw95mu88csbWHd8HY7kHME/Bv0DbVzaSD00u2FQJJspKDbi50sFOJZjwJn9WVWC4dQWFAzLjUYUnziB4iS95REVaWkw5VX8UaBSwUmnheczEyynkYaGQtWm+f6SIZJKU37GJzVvAydN5zVgRE2Qt8Yb60esx8a0jViTvAZP73oa70a+i0c6PSL10OyCQZHqraYjhnIAvTq0rGBounoVBr3lpjPFx1NRkp5uvS2/wtsbzr17w2vyJMtNZ/r1g9zJSeIRExGRVHgNGFHTJZPJ8EKfF/BQu4fw2k+v4a/7/4qpvabileBXoFI0r793GRTpvhUUG3HwrmsM7z5i+KDSiKB2LvAP0kk9XLsRBAEl6ekoPnIEhuRkFKedhOmPPywb5XKou3WDxxNPQBMUCNfQUKg6d+a1H0REJMJrwIiatr6t++Kbsd/grd/ewhenv8Cx3GNYOXglOrl3knpoNsOgSDW6n2B49xHDiyfOSzxq2zMXFsKgP4rio0dRfPw4Sk6dQnlhIQBA5uYGjb8/PB57DJrgYLiEBEPh5ibxiImIiIjI3tzUbvhwyIf46sxXWKlfiWd2P4M3w97EqO7N40ZfDIpkVZ9geDd3n6b9HD9BEGC8dMnyiIpjySg+cQJlmZlAeTkAQNmpE1wHPgyXwEBoQkPhrNXypjNERERELdjEnhMR3DYYr/70Khb+uhCHcw7jzbA34aRs2pcaMSi2YLYIhk1deWkpio8fR7FeD0PKcZSkpcF8/bplo5MTnHU6eE15Fi7BwXAJCYHygQekHTAREREROZweXj2wffR2vJf4Hnae34nUvFSsemQVenj1kHpo9cag2IIwGALGK1dgOHIEhmPHUJyaitL0s0DFg5GVrVtD068fNP37Wx5R0acP5KrmWQciIiIisi0npRPejXwX4e3C8V7ie5j87WSM7DoSBy8fbJLPoGVQbMZaejAUystRfPKk5REVyckoTkuDKTvbslGphLp7N3j+6U/QBAXBJSwU6o4dpR0w2Z13B/4/JiIiuh9qX1+ph9Bkjeo+Cn3b9MXMhJn49/l/W9uzi7Kx9OBSS58mEBYZFJuRlh4MTTdvoVifdPsRFadPQzAYAADyVq3gHBAAj3F/gktICDSBgVC4uEg8YiIiIiJqjjq5d0K5UF6lvcRcgv899r8MimQbO5MvY9ne08grKEUHTw1eG6nDuMCOLToYCoKAssxMGJKSLKeRnkiD8cIFQBAAAKouXeA+dCg0gf3hGhoKdY8efEQFkQ25t20r9RCIiIgcWq4ht9r2nKKcRh5J/TAoOridyZfxxo4TKDaaAQCX84vx6vYU/OO708i9VdpigqG5tBTFx46hWH8UxSnJKD55CuX5+QAAmbMznHv1gttzz1luOvNQCJSenpKOl4iIiIhatnau7ZBdlF1te1PAoOjgViSkW0NiJbMA5BWUOWQwLPvvfy2fNPBaMGN29u1HVKSmovTcOcBkAgAo27WD60MhFTedeQjO/v6QK/mjTERERESOIyooCksPLkWJucTa5qxwRlRQlISjun/869rBZeUXV9tuKhfw9tjejTwa+yg3mVBy8iSKk5JgSE5BSVoaTLkVh+qVSjg9+CA8n3oKLsFBcAkNhapd03gXhoiIiIharsrrEFfpV/Gup2R7HTw1uFxNWOzg2XQfbG+6cQOGJD0MR4+iJPU4Sk6fgVBieadF7uUJjX8APJ9+GpqQYGiCgqBwatoPKyUiIiKilmlUt1EI8AkAAPh5+Ek7mDpiUHRwr43Uia5RBACNSoHXRuokHFX1bu7ejZvLP0D5tWso6NABbebPQ6vRo1F27hyKjiRZHlFx4gSMly5ZXiCTQdW1K1qNfNTyiIqHHoK6a1fedIaIiFoEr9ZtpB4CEVGNGBQd3LhAy7V+KxLSkZVfLLrrqSO5uXs3she/ZT0yaMrKQtbr0cha/BZQ0SZzcYGzvz/cRwyHS3AwNMEhUHq0knLYREREknHq2lXqIRAR1YhBsQkYF9jR4YLh3a78v/9vDYlWggAIAtpEvw6XipvOyORyaQZIRERERET3jUGRbMKUXfXWvwCAsjL4PP984w6GGh3fFSciImpevBt4B3tq+hzm8E5iYiKmTZuGwMBAhISEYObMmTh16lSd9rFt2zaMGTMGffv2RWRkJP72t7/h2rVrdhox3UnZvn2d2omIiIiIyHE5RFA8cOAA/vznPyM9PR3jx4/H2LFjkZqaismTJyM1NfW+9rFy5UosWbIEZWVlmDp1KsLDwxEXF4fJkyfj5s2bdp4BtZk/DzJnZ1GbzNkZbebPk2ZARERERERUb5KfelpWVoYlS5bAyckJ27ZtQ7du3QAAU6ZMwVNPPYWlS5dix44dte7j7Nmz+OSTT6DVarF9+3ZoNJZHRwwcOBDR0dH45z//iTfeeMPuc2nJPMaMAQBcWbESprw8KNu3R5v586ztRERS4GnRRERE9SP5EcWDBw8iJycHY8eOtYZEAOjevTvGjBmDkydP3vMU1Li4OJSXl+Oll16yhkQAGDduHPz8/LBz506YTCa7zYEsPMaMQefNn6Lb3m/x4P4fGBKJmjE/D78m9zwoIiIiun+SB0W9Xg8AGDBgQJVtlW2VfWpy9OjRWveRn5+P8+fPN3SodB+cunblO/hERM0U3yAgIqq7pvq7U/KgeKni4eudO3eusq2yrbJPTS5evAhXV1d4e3vXex9ERERERERkIXlQLCwsBAC4ublV2VbZVlBQUOs+ioqKqn19XfZBREREREREFpIHRSIiIiIiInIskgfFyiN+lUcW71TZ5u7uXus+XF1dq319XfZBREREREREFpIHxdquIazt+sU7denSBUVFRbh+/Xq990FEREREREQWkgfFkJAQAMChQ4eqbKtsq+xTk+Dg4Fr34enpiR49ejR0qERERERERC2C5EExIiIC7dq1w65du5CZmWltz8jIwO7du+Hv7w9/f38AgNFoREZGBrKyskT7GD9+PORyOdavX4/i4mJr+86dO3HhwgWMGzcOSqWycSZERERERETUxEmentRqNZYuXYrZs2dj0qRJGD16NABgz549KC8vx9tvv23tm5ubiyeeeAKhoaH4/PPPre1arRYzZsxATEwMxo0bh2HDhiE3Nxfx8fHo0qULZs+e3ejzIiIiIiIiaqokD4oAMGTIEGzatAlr165FXFwcZDIZgoKCMH/+fAQEBNzXPhYsWIBOnTphy5Yt+Pzzz+Hu7o5x48bh1VdfhYeHh51nQERERERE1Hw4RFAEgPDwcISHh9fax9fXF+np6dVuk8lkmDRpEiZNmmSP4REREREREbUYDhMUHYXZbAYA5OTkSDwSIiIiIiIi+6jMO5X5524MinfJy8sDAEyZMkXikRAREREREdlXXl4eunTpUqVdJgiCIMF4HFZJSQnS0tLQunVrKBQKqYdDRERERERkc2azGXl5eejduzecnZ2rbGdQJCIiIiIiIhHJn6NIREREREREjoVBkYiIiIiIiEQYFImIiIiIiEiEQZGIiIiIiIhEGBSJiIiIiIhIhEGRiIiIiIiIRBgUiYiIiIiISIRBkYiIiIiIiESUUg+ApJOTk4P4+HgcOHAAmZmZuHHjBnx8fBAREYHZs2ejU6dOVV6TkJCAmJgYnD17Fs7OzggPD8eCBQuq7QsA27Ztw5YtW3Dx4kW4u7tj8ODBeOWVV+Dj4yPqp9frkZCQgCNHjuDy5cswGo3o2rUrxo4di2nTpkGlUtmlBrbiSLW8W3Z2NkaNGoWioiLMmjULCxYssMmc7ckR65mVlYX169fjl19+QV5eHjw8PNCzZ0/MmjUL4eHhNp2/rTlaPf/44w+sW7cOer0eOTk58Pb2hr+/P2bNmoXAwECbz9/W7FnPa9euYfv27Thx4gROnDiBK1euoF+/fti+fXuN4zGZTPjkk0+wY8cOZGVlwcfHB4899hjmzJkDV1dXm8/f1hypnlyLbPuzeSeuRbapJ9ci29Wzqa9FjUEmCIIg9SBIGitXrsTHH3+M7t27IyQkBO7u7khLS8Phw4fRqlUrbNmyBVqt1tp/69atWLp0Kdq2bYvHH38cBQUF+Pbbb6HRaBAbG1vlH23l/v38/DBs2DDk5uYiPj4evr6+iI2NhYeHh7VvZGQkbt68idDQUOh0OpSWluLAgQO4fPkyIiMjERMTA7nccQ+AO1It7zZr1izo9XoYDIYmszg7Wj1TUlLwwgsvoLy8HEOHDkXHjh2Rn5+PtLQ0jBgxAn/5y18apS715Uj1PH/+PJ555hmUlpZi2LBh6Ny5M3JycrBv3z4YjUasXr0aw4cPb7Ta1Ic965mYmIjp06dDoVCge/fuOHv27D3/2Jk/fz727t2LgIAADBgwAJmZmdi/fz8CAwPx2WefQa1W27UeDeVI9eRaZNufzTtxLWp4PbkW2a6ezWEtahQCtVj79u0Tjh49WqV906ZNglarFWbMmGFtu3btmtC/f38hMjJSuHr1qrX98OHDgk6nE15++WXRPtLT04WePXsKo0ePFgwGg7U9Li5O0Gq1wrJly0T9P/74Y9F+BUEQiouLhQkTJgharVbYs2dPg+Zqb45UyzvFxcUJvXr1EmJiYgStViusWLGiIdNsNI5Uz/z8fCEyMlIYOXKkkJOTU2VMRqOx3vNsLI5Uz+joaEGr1Qp79+4VtaekpAharVaYMGFCg+baGOxZz7y8PCEpKclay3vV5MCBA9bvaTKZrO2rV68WtFqtsHnz5nrPs7E4Uj25FtmulnfiWtTwenItsrBVPZvDWtQYGBSpCrPZLPTr10/o37+/te2LL74QtFqt8K9//atK/+eff17o1auXcO3aNWvb8uXLa1xUH330USE0NPS+fqnt2bNH0Gq1wttvv13P2UhLylrm5eUJoaGhwrJly4TDhw83qcW5JlLUc82aNYJWqxUSExNtPBvpSVHPqVOnClqtVigsLKzSPzQ0VBg+fHhDpyUZW9Tzbvf6g2XOnDmCVqsVkpOTRe3FxcVCUFCQMGbMmLpPxEFIUc+acC2q6n5rybXINvXkWnSbLerZnNciW3Lc8ydIUgqFAgqFwvq1Xq8HgGrPfx8wYADMZjOSk5OtbUePHrVuq65/fn4+zp8/f89xKJVK63iaKqlq+c4778DV1RVRUVENnoMjaex6JiQkwMvLC6GhoUhNTcXGjRuxadMmJCUl2WxOUmrsenbr1g0A8PPPP4v6Hj9+HPn5+QgNDW3AbKTX0HrW1dGjR+Hi4oK+ffuK2p2dnREUFIT09HQUFhbWe/9Sa+x61oRrUf1xLbJNPbkW3WaLejb3tchWeDMbquKHH35AYWEhRowYYW27ePEiAKBz585V+le2Xbp0SdTf1dUV3t7etfbv2bNnrWPZuXMngOp/UTQFUtVy3759SEhIwIYNG+Di4mKbyTiAxq5nWVkZzp8/D39/fyxevLjKtQ6hoaFYs2ZNrdeIOjIpfj5ffPFF/PTTT1iwYAHi4+PRpUsX63UhAwcOxGuvvWa7CTYyW9SzLoqKinD16lVotdpqr5ur3P/FixcREBBQr+8hpcauZ224FtUP1yKI2upbT65FYrb4+WzOa5EtMSiSyNWrV/Huu+9CrVZj7ty51vaioiIAgJubW5XXVLYVFBSI+lf3h2NN/auza9cu7N+/H8HBwRg6dGjdJuIApKrlzZs38c4772D06NF45JFHGj4RByFFPW/evAmz2YxTp04hMzMTK1aswJAhQ3Dt2jWsWrUK+/btw+LFi/HRRx/ZZpKNSKqfzw4dOuCrr77CnDlzkJCQYG3v2LEjnnzySXh6etZ/UhKyVT3rovJIYXX7tsX+pSRFPWvCtah+uBbd1tB6ci0Ss8XPZ3Ndi2yNp56SlcFgwOzZs5Gbm4u33npLdOepxqbX67F48WK0adMGq1atgkwmk2ws9SFlLZcvXw6j0YhFixY12ve0N6nqWV5eDgAwm82YN28exo4dC3d3d/j5+WHVqlXo2LEj9u3bh6ysrEYZj61I+fOZkZGByZMnQy6XIzY2FikpKUhISEBwcDDmz5+PDRs2NNpYbMWRfnc2B45UT65F9ce1yHa4Ftlec1yL7IFBkQAAJSUleOmll3D8+HG8/vrrmDBhgmh75bO4qrvWpbLN3d1d1L+m62Kq63+n1NRUvPjii3BxccGnn36K9u3b131CEpKylocPH8aOHTvw+uuv3/P5ik2FlPW883VDhgwR9VWr1YiMjIQgCDh16lRdpyUZqf+tL1q0CNevX8fatWvRt29faDQa+Pn54YMPPoBOp8PatWub1DV1tq5nXVS+q17f37WOSMp63o1rUf1rybVIrKH15FokZot/681tLbIXBkVCWVkZXn75ZSQmJmLOnDl44YUXqvTp0qULgOrPB69su/M88i5duqCoqAjXr1+/r/6VTp06hZkzZ0KhUGDTpk3o3r17/SYlEalreebMGQCWX4A6nc763/Tp0wEAH3/8MXQ6Hf7+9783ZJqNRup6uri4oE2bNgCqX5Aq/1AvKSmp07ykInU9CwsLkZKSgm7dulX541EulyM4OBglJSX4/fff6znDxmWPetaFq6srHnjgAfz3v/+1HnGobv+VY3B0UtfzTlyLGlZLrkViDa0n1yKxhtazua1F9sSg2MIZjUZERUXh119/xcyZM/HXv/612n4hISEALO8S3u3QoUNQKBQIDAy0tgUHB1u3Vdff09MTPXr0ELWfO3cOM2bMgMlkQkxMzD1vdONoHKGWWq0WTz/9dJX/Kq8P0el0ePrpp0X7d1SOUE8A1jufZWRkVOmfmZkJAE3iSIMj1LOsrAwAkJ+fX+33vnHjBgBApVLd56ykY6961lVwcDAMBgNSU1NF7SUlJTh27Bh0Ol2N1zA6EkepJ8C1CGh4LbkWidniZ5Nr0W0NrWdzWovsTurnc5B0TCaTEBUVJWi1WuHdd9+ttW9NDz5NTEy0yUO4f//9dyEyMlLo16+fkJSUZIPZNS5HqmV1mtqzqxypnomJiYJWqxWef/55obS01Nqu1+uFnj17CoMGDRLKysoaMl27c6R6jhgxQtBqtUJcXJyo/fTp00KfPn2EsLAwh39wtD3rebd7PQvswIED1gdVm0wma/vq1asFrVYrbN68+T5nJR1HqifXItvVsjpci2p2r3pyLbKwVT2bw1rUGGSCIAhSh1WSxkcffYS1a9fCy8sLzz77bLUX6c+ZM8f6+Zdffom3334bbdu2xeOPP47CwkLs2bMHGo0GsbGx6NSpk+i1K1asQExMDPz8/DBs2DDk5uYiPj4evr6+iI2NFd3GeciQIcjKysKAAQOsRyju1KtXLwwfPtyGs7ctR6pldRITEzF9+nTMmjULCxYssM2k7cjR6rl06VJs3boVDz74ICIiInD9+nUkJCRAEASsXbvW4e/o50j1/P777zFnzhyUl5dj0KBBePDBB5Gbm4t9+/ahrKwMH3zwAcaNG2e3WtiCveu5cOFC6+dxcXHw8vLC4MGDAQBeXl6Ijo4W9Z83bx7i4+MREBCAiIgIZGRkYP/+/ejfvz8+//xzqNVqG87e9hypnlyLbPuzeTeuRQ2rJ9ci29WzOaxFjYFBsQVbuHAh4uLiau2Tnp4u+vq7775DTEwMzp07BycnJ4SHh2PBggXVnicuCAK++uorbNmyBRcuXIC7uzsGDx6MV199tco54TqdrtZxjB8/HsuXL7/PmTU+R6pldZra4uxo9RQEAV9++SW2bduGCxcuwMnJCUFBQXj55ZfRr1+/hk22EThaPfV6PWJiYpCSkoJbt27Bzc0Nffr0wYwZMxAZGdmwyTYCe9eztt+HHTt2xP79+0VtRqMRn3zyCXbs2IGsrCz4+Pjgsccew9y5c603hHBkjlRPrkW2/dm8G9cisbrWk2uRbevZ1NeixsCgSERERERERCK8mQ0RERERERGJMCgSERERERGRCIMiERERERERiTAoEhERERERkQiDIhEREREREYkwKBIREREREZEIgyIRERERERGJMCgSERERERGRCIMiERERERERiTAoEhERERERkQiDIhEREREREYkwKBIREdXBuXPnoNPpMHny5Br7zJ07FzqdDocOHbK2nTlzBgsWLMCgQYPQu3dvDBgwAHPnzsWZM2eqvP7atWtYt24dpkyZgocfftjaf/bs2UhJSan2e+p0OgwdOhSFhYV4//33MXToUAQEBGD16tUNnjMREbU8MkEQBKkHQURE1JRMmzYNR44cwe7du6HVakXbcnNzMXToUHTq1AnfffcdAODbb79FdHQ0ZDIZhgwZAl9fX+Tk5OA///kPZDIZ1q9fj4iICOs+fvzxR8ydOxdhYWHw9fWFq6srLl26hB9//BGCIGDdunUYNGiQ6PvqdDq0bt0abdq0wc2bNxEREQE3NzdotVqMHz/e/kUhIqJmRSn1AIiIiJqaKVOm4MiRI9i6dSuWLFki2hYbGwuTyYRJkyYBAC5duoQ33ngDPj4+2LJlC3x9fa19z549i4kTJyI6Oho//PAD1Go1AKB///44ePAg3N3dRfs+c+YMJk+ejPfff79KUASAvLw89OjRA1988QVcXFxsPW0iImpBeOopERFRHQ0fPhxt27bFrl27UFRUZG03m82IjY2Fs7MznnzySQDAl19+idLSUkRHR4tCIgBotVpMmDABV65cEZ2m6uXlVSUkAkDPnj0RFhaGzMxMZGVlVTu2hQsXMiQSEVGD8YgiERFRHSmVSkycOBEfffQR9uzZg4kTJwKwnDKak5ODp556Cq1atQIAHDt2DACQnJyMjIyMKvu6cOECACAjIwOPPPKItf3AgQPYtm0b0tLScOPGDZhMJtHrcnNz0aFDB1Gbk5MTdDqdzeZJREQtF4MiERFRPUycOBHr1q3D1q1brUFx69atACC60U1+fj4A4LPPPqt1fwaDwfr55s2bsWzZMri4uODhhx+Gr68vNBoNZDIZvv/+e5w5cwZlZWVV9uHj4wOZTNbQqRERETEoEhER1ccDDzyAkSNHYs+ePUhJSYG3tzd+++039O7dG3369LH2qzyF9Oeff0bbtm3vuV+TyYQ1a9bA2dkZO3bsQNeuXUXba7rrKQCGRCIishleo0hERFRPU6ZMAWA5krht2zYIgoBnn31W1CcwMBAAoNfr72ufN27cwK1bt9C9e/cqIdFgMODkyZM2GDkREVHtGBSJiIjqKSgoCP7+/oiPj8c333wDDw8PjBo1StRn6tSpUKvVWLFiRbXPTBQEAXq93noqqY+PDzQaDS5cuIArV65Y+5lMJrz//vu4ceOGfSdFREQEnnpKRETUIM8++yzefPNNlJaW4rnnnoOzs7Nou5+fHz744ANER0dj/PjxiIiIQLdu3SCXy5GdnY3U1FRkZ2cjKSkJarUacrkc06ZNw4YNGzB+/HiMHDkSMpkMiYmJuH79OsLCwpCYmCjRbImIqKVgUCQiImqAMWPG4L333kNpaan12Yl3e+KJJ6DT6fDpp5/i0KFDOHLkCFQqFVq3bo2goCAMGzYMbm5u1v5RUVHw9vZGbGwsvv76a7i7uyMiIgLz5s3D6tWrG2tqRETUgskEQRCkHgQREVFTdfr0aYwbNw4DBgzAp59+KvVwiIiIbILXKBIRETXAhg0bAADTpk2TeCRERES2w1NPiYiI6ig9PR0HDhxAeno69u7di/79+2Po0KFSD4uIiMhmGBSJiIjq6OTJk/jwww/h6uqKIUOGYMmSJXyGIRERNSu8RpGIiIiIiIhEeI0iERERERERiTAoEhERERERkQiDIhEREREREYkwKBIREREREZEIgyIRERERERGJMCgSERERERGRyP8BZl87Kj7CB/AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"notebook\",font_scale=1.7)\n",
    "nations = [\"IT\", \"FR\", \"ES\",\"DE\", \"NL\", \"AT\"]\n",
    "\n",
    "colors = sns.color_palette(\"tab10\",n_colors=len(nations))\n",
    "\n",
    "fig = plt.figure(figsize=(13,5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for i,nation in enumerate(nations):\n",
    "\n",
    "    years = df_scores_in_time_nations[\"score\"][\"mean\"][nation].index.values\n",
    "    means = df_scores_in_time_nations[\"score\"][\"mean\"][nation].values\n",
    "    errs =df_scores_in_time_nations[\"score\"][\"err\"][nation].values\n",
    "\n",
    "    ax.errorbar(years.astype(int), means, errs, color=colors[i],alpha=0.2)\n",
    "    ax.plot(years.astype(int), means,'-o', color=colors[i], label=nation)\n",
    "\n",
    "ax.legend(ncol=3)\n",
    "ax.set_ylabel(\"avg. score\")\n",
    "ax.set_xlabel(\"year\")\n",
    "ttt= ax.set_xticks(range(2002,2020,2))\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./main_text_figures/figure5.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 6 \n",
    "\n",
    "Figure 6 shows the Pop. Score for some parties in each year. We simply select the desired parties from the \"df_scores_in_time\" dataframe and plot the party score against the year for which we computed the score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"notebook\",font_scale=1.7)\n",
    "\n",
    "fig = plt.figure(figsize=(13,5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "parties_to_plot = ['Austrian People’s Party', \"Green Left\", 'The Left', \"People's Party\"]\n",
    "labels_to_plot = [\"ÖVP\",\"GL\",\"Linke\", \"PP\"]\n",
    "\n",
    "\n",
    "for label,party in zip(labels_to_plot,parties_to_plot):\n",
    "    df_sub = df_scores_in_time[df_scores_in_time.party==party].copy()\n",
    "    if party == \"Socialist Party\": df_sub = df_sub[df_sub.nation==\"NL\"].copy()\n",
    "    df_sub = df_sub.loc[df_sub.year.sort_values().index]\n",
    "    \n",
    "    years = df_sub.year\n",
    "    score = df_sub.score\n",
    "    ax.plot(years.astype(int), score.values,'-o', label=label)\n",
    "    \n",
    "ax.legend(ncol=2)\n",
    "ax.set_ylabel(\"score\")\n",
    "ax.set_xlabel(\"year\")\n",
    "ttt= ax.set_xticks(range(2002,2020,2))\n",
    "ax.set_ylim(0,1)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./main_text_figures/figure6.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table I\n",
    "\n",
    "This table shows the training results of different classification algorithms on the Italian Manifesto datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>AUC_valid</th>\n",
       "      <th>AUC_valid_err</th>\n",
       "      <th>F1_valid</th>\n",
       "      <th>F1_valid_err</th>\n",
       "      <th>AUC_test</th>\n",
       "      <th>F1_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NeuralNetwork</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Algorithm  AUC_valid  AUC_valid_err  F1_valid  F1_valid_err  \\\n",
       "0          Logistic       0.82            0.1      0.62          0.01   \n",
       "1  GradientBoosting       0.84            0.1      0.53          0.01   \n",
       "2     NeuralNetwork       0.85            0.1      0.64          0.01   \n",
       "\n",
       "   AUC_test  F1_test  \n",
       "0      0.83     0.61  \n",
       "1      0.85     0.61  \n",
       "2      0.86     0.64  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_nations = [\"IT\"]\n",
    "models = [\"Logistic\",\"GradientBoosting\", \"NeuralNetwork\"]\n",
    "target_score = \"AUC\"\n",
    "df_selected_trainings = df_training_res[(df_training_res.nation.isin(selected_nations)) & (df_training_res.model_type.isin(models))&(df_training_res.target_score==target_score)].copy()\n",
    "df_selected_trainings = df_selected_trainings[[\"model_type\",'AUC_valid',\"AUC_valid_err\",'F1_valid',\"F1_valid_err\",\"AUC_test\",\"F1_test\"]]\n",
    "sort_indexes = [df_selected_trainings.loc[df_selected_trainings.model_type==model].index[0] for model in models]\n",
    "df_selected_trainings = df_selected_trainings.loc[sort_indexes]\n",
    "df_selected_trainings.AUC_valid = df_selected_trainings.AUC_valid.apply(lambda v: round(v,2))\n",
    "df_selected_trainings.AUC_valid_err = df_selected_trainings.AUC_valid_err.apply(lambda v: max(round(v,2),0.1))\n",
    "df_selected_trainings.F1_valid = df_selected_trainings.F1_valid.apply(lambda v: round(v,2))\n",
    "df_selected_trainings.F1_valid_err = df_selected_trainings.F1_valid_err.apply(lambda v: max(round(v,2),0.01))\n",
    "df_selected_trainings.AUC_test = df_selected_trainings.AUC_test.apply(lambda v: round(v,2))\n",
    "df_selected_trainings.F1_test = df_selected_trainings.F1_test.apply(lambda v: round(v,2))\n",
    "\n",
    "\n",
    "df_selected_trainings.index = range(len(df_selected_trainings))\n",
    "df_selected_trainings = df_selected_trainings.rename(columns={\n",
    "    \"model_type\":\"Algorithm\"})\n",
    "\n",
    "df_selected_trainings.to_html(\"./SI_figures/tableI.html\")\n",
    "\n",
    "df_selected_trainings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table M of SI\n",
    "\n",
    "This tables show the result of the Random Forest classifiers trained on the Italian Manually coded dataset (IT_manual) and the Italian Leader's Speeches dataset (IT_speeches)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>AUC_valid</th>\n",
       "      <th>AUC_valid_err</th>\n",
       "      <th>F1_valid</th>\n",
       "      <th>F1_valid_err</th>\n",
       "      <th>AUC_test</th>\n",
       "      <th>F1_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IT_speeches</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IT_manual</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dataset  AUC_valid  AUC_valid_err  F1_valid  F1_valid_err  AUC_test  \\\n",
       "0  IT_speeches       0.81            0.1      0.10          0.01      0.83   \n",
       "1    IT_manual       0.84            0.1      0.42          0.02      0.84   \n",
       "\n",
       "   F1_test  \n",
       "0     0.64  \n",
       "1     0.40  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_nations = [\"IT_speeches\", \"IT_manual\"]\n",
    "models = [\"RandomForest\"]\n",
    "target_scores = [\"AUC\", \"F1\"]\n",
    "df_selected_trainings = df_training_res[(df_training_res.nation.isin(selected_nations)) &\\\n",
    "                                        (df_training_res.model_type.isin(models))&\\\n",
    "                                        (df_training_res.target_score.isin(target_scores))].copy()\n",
    "df_selected_trainings = df_selected_trainings[[\"nation\",'AUC_valid',\"AUC_valid_err\",'F1_valid',\"F1_valid_err\",\"AUC_test\",\"F1_test\"]]\n",
    "df_selected_trainings.AUC_valid = df_selected_trainings.AUC_valid.apply(lambda v: round(v,2))\n",
    "df_selected_trainings.AUC_valid_err = df_selected_trainings.AUC_valid_err.apply(lambda v: max(round(v,2),0.1))\n",
    "df_selected_trainings.F1_valid = df_selected_trainings.F1_valid.apply(lambda v: round(v,2))\n",
    "df_selected_trainings.F1_valid_err = df_selected_trainings.F1_valid_err.apply(lambda v: max(round(v,2),0.01))\n",
    "df_selected_trainings.AUC_test = df_selected_trainings.AUC_test.apply(lambda v: round(v,2))\n",
    "df_selected_trainings.F1_test = df_selected_trainings.F1_test.apply(lambda v: round(v,2))\n",
    "\n",
    "\n",
    "df_selected_trainings.index = range(len(df_selected_trainings))\n",
    "df_selected_trainings = df_selected_trainings.rename(columns={\n",
    "    \"nation\":\"Dataset\"})\n",
    "\n",
    "df_selected_trainings.to_html(\"./SI_figures/tableM.html\")\n",
    "\n",
    "df_selected_trainings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure C and D of SI\n",
    "\n",
    "In this figure we show the comparisons between the score computed with manifestos, and the scores computed using leaders' speeches and manually coded data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_IT_speeches = pd.read_csv(\"./scores/global_scores_IT_speeches.csv\")\n",
    "df_IT_manual = pd.read_csv(\"./scores/global_scores_IT_manual.csv\")\n",
    "\n",
    "\n",
    "#########\n",
    "\n",
    "df_merged = pd.merge(df_global_scores[df_global_scores.nation==\"IT\"].copy(),df_IT_speeches, on = [\"party\"], suffixes=(\"\", \"_speeches\"))\n",
    "df_merged = pd.merge(df_merged,df_IT_manual, on = [\"party\"], suffixes=(\"\", \"_manual\"))\n",
    "#################\n",
    "\n",
    "\n",
    "mappings = {\n",
    "    \"PD\":\"PD\",\n",
    "    'Civil Revolution':\"RC\",\n",
    "    \"Northern League\":\"LN\",\n",
    "    \"SEL\":\"SEL\",\n",
    "    \"Civic Choice\":\"SC\",\n",
    "    \"UDC\":\"UdC\",\n",
    "    \"Democratic Center\":\"CD\",\n",
    "    \"M5S\":\"M5S\",\n",
    "    \"LeU\":\"LeU\",\n",
    "    \"PaP\":\"PaP\",\n",
    "    \"Forward Italy\":\"FI\",\n",
    "    'PdL':'PdL',\n",
    "    'Italy of Values':\"IdV\",\n",
    "    'Brothers of Italy':\"FdI\",\n",
    "    'Casapound':\"CPI\",\n",
    "    'House of Freedoms':\"CdL\",\n",
    "    \"The Olive Tree\":\"UL\",\n",
    "    \"Communist Refoundation Party\":\"CRP\"\n",
    "}\n",
    "\n",
    "df_merged[\"party\"] = [mappings[party] for party in df_merged.party]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_context(\"notebook\", font_scale=1.8)\n",
    "\n",
    "fig  = plt.figure(figsize=(8,5))\n",
    "\n",
    "r = round(scipy.stats.pearsonr(df_merged.score, df_merged.score_speeches)[0],2)\n",
    "ax = sns.regplot(x=\"score\", y=\"score_speeches\", data=df_merged,label=r\"$\\rho = {}$\".format(r) )\n",
    "\n",
    "def label_point(x, y, val, ax):\n",
    "    a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)\n",
    "    for i, point in a.iterrows():\n",
    "        ax.text(point['x']+.02, point['y'], str(point['val']))\n",
    "\n",
    "label_point(df_merged.score, df_merged.score_speeches, df_merged.party, plt.gca())  \n",
    "\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.set_xlabel(\"Score\")\n",
    "ax.set_ylabel(\"Speeches Score\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./SI_figures/figure_C.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_context(\"notebook\", font_scale=1.8)\n",
    "\n",
    "fig  = plt.figure(figsize=(8,5))\n",
    "\n",
    "r = round(scipy.stats.pearsonr(df_merged.score, df_merged.score_manual)[0],2)\n",
    "ax = sns.regplot(x=\"score\", y=\"score_manual\", data=df_merged,label=r\"$\\rho = {}$\".format(r) )\n",
    "\n",
    "def label_point(x, y, val, ax):\n",
    "    a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)\n",
    "    for i, point in a.iterrows():\n",
    "        ax.text(point['x']+.02, point['y'], str(point['val']))\n",
    "\n",
    "label_point(df_merged.score, df_merged.score_manual, df_merged.party, plt.gca())  \n",
    "\n",
    "ax.set_xlabel(\"Score\")\n",
    "ax.set_ylabel(\"Manaully Coded Score\")\n",
    "ax.legend(loc=\"upper left\")\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"./SI_figures/figure_D.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure H of SI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "party_labels = {\n",
    "    \n",
    "    \"DE\":{\n",
    "        \"Free Democratic Party\":\"FDP\",\n",
    "        \"The Left\":\"Die Linke\",\n",
    "        \"Alternative for Germany\":\"AfD\",\n",
    "        \"Christian Democratic Union-Christian Social Union\":\"CDU-CSU\",\n",
    "        \"Alliance‘90-Greens\": \"B’90/Die Grünen\",\n",
    "        \"Social Democratic Party of Germany\": \"SPD\"\n",
    "    },\n",
    "    \n",
    "    \"IT\":{\n",
    "        \"PD\":\"PD\",\n",
    "        \"Forward Italy\":\"FI\",\n",
    "        \"M5S\":\"M5S\",\n",
    "        \"UDC\":\"UDC\"\n",
    "    },\n",
    "    \n",
    "    \"FR\":{\n",
    "        \"Union for a Popular Movement\":\"DLR\",\n",
    "        \"National Front\":\"FN\",\n",
    "        \"Republic Onwards!\":\"LaREM\",\n",
    "        \"Socialist Party\":\"PS\",\n",
    "        \"Democratic Mouvement\": \"MoDem\",\n",
    "        \"The Greens\": \"LV\"\n",
    "    },\n",
    "    \n",
    "    \"ES\":{\n",
    "        \"People's Party\":\"PP\",\n",
    "        \"We can\":\"Podemos\",\n",
    "        \"Spanish Socialist Workers’ Party\": \"PSOE\",\n",
    "        \"Citizens\": \"C’s\",\n",
    "    },\n",
    "    \n",
    "    \"NL\":{\n",
    "        \"Party of Freedom\":\"PVV\",\n",
    "        \"Forum for Democracy\":\"FvD\",\n",
    "        \"Socialist Party\":\"SP\",\n",
    "        \"Green Left\":\"GL\",        \n",
    "        \"Christian Democratic Appeal\":\"CDA\",\n",
    "        \"Labour Party\": \"PvdA\",\n",
    "        \"Christian Union\": \"CU\",\n",
    "        \"Democrats‘66\": \"D66\",\n",
    "        \"People’s Party for Freedom and Democracy\": \"VVD\",\n",
    "        \"Party for the Animals\": \"PvdD\",\n",
    "        \"Reformed Political Party\": \"SGP\",\n",
    "    },\n",
    "    \n",
    "    \"AT\":{\n",
    "        \"Austrian Freedom Party\":\"FPO\",\n",
    "        \"Austrian People’s Party\":\"OVP\",\n",
    "        \"The Greens\":\"GRÜNE\",\n",
    "        \"Austrian Social Democratic Party\": \"SPÖ\",\n",
    "        \n",
    "    }\n",
    "    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wyWRSenp6ibaYmBgZhqH27duX6h8YGCiHw1G1FQIAAAB1CHNsAAAAeEK5QXB4eLh27NhRom3r1q0ymUzq2LFjqf4pKSlq0KBB1VYIAAAA1CHMsQEAAOAJ5QbBffr00Y4dO7Rs2TJJUmJiohYuXCiTyaQrrriiVP/du3eXuugFAAAAgNOYYwMAAMATyg2C77jjDnl7e+uJJ55Qr169NHDgQMXFxWnIkCFq2LBhib4nTpzQjh071L1792otGAAAALiYMccGAACAJ5QbBDdu3FjvvPOOmjRpolOnTkmShg4dqn/+85+l+i5cuFAul0v9+vWrnkoBAACAOoA5NgAAADzBeq4OvXr10urVq5WcnCx/f3/Z7fYy+91+++2aPHmy/P39q7xIAAAAoC5hjg0AAICads4guEhISEi5x318fC64GAAAAOBSwhwbAAAANaXCQTAAADh/ezYf16ZPY5WRnCv/EC/1vb6l2vTm4k8AAAAAgJpBEAwAQDXbs/m41s3bLUeeS5KUkZyrdfN2SxJhMAAAAACgRpR7sTgAAHDhNi2LdYfARRx5Ln2/ZJ+cDtdZzgIAAAAAoOqwIhgAgCqWm+3QsX2pOrYvVfF7U5WRkltmv8y0PM18YL18A+zyr+cl/3reZ9x6yT/EW35Bdpkt/O0WAAAAAHD+CIIBALhA2Rl5OrY3TfF7UxW/L1UnjqTLMCSzxaSwpoGyeVuUn+MsdZ6Xn1WdBzRWRkqOMlNylZKQpSO7k0v1NZkk30C7/EMKA+Jgb/mHlAyMfYO8ZDabauolAwAAAAAuMgTBAABUUmZqbkHouzdVcXtTlXIsU5JksZkV3iJQPYc3V0TrYDVsHiib3VJqj2BJstrNuvKPbcrcIzg326GMlBxlpOQqIzlHGam57vsn4zJ16LeTpbaaMJlN8gs628riguDYN8AuE2ExAAAAAFySCIIBACiHYRhKP5njDn3j96bqVFK2JMnmbVGjlsGK7t1QEa3rKaxpgCzW0ls4FIW9mz6NVUZyrvxDvNT3+pZnvVCcl49VXj7+qh/hf9aacrMcBeFwUWDsvs1V0pF0Hdh+Qs78kmGx2WySX7DX6dXExe8XBsY+/jbCYgAAAACogyodBCcnJ+vYsWNKSUmRt7e36tevr6ZNm8psZu9CAMDFzzAMpSZkKW5PQeh7bN/pPX69/KyKaBWsTldFKqJ1sBpE+Vd47942vcPPGvxWlslkkrefTd5+NjWIOntYnJOZ7w6HT68szlFGcq4SDqQpNjVXLodR4jyz1VQQEJe1X3HhymJvP5tMJsJioCoxxwYAAEB1q1AQHBsbq8WLF2vDhg2KjY0tddzPz089e/bU0KFDNWzYMNnt9iovFACA6uByGToZl1EQ+hbu8Zudni+pYF/eiDbBimgVrIjWwQpp5HfRrJY1mUzy8bfLx9+u0MYBZfYxDEPZ6fklVhMXX2F8LDZNmamJcjlLhsUWm7nkauJgr9P7FxcGxl6+VsJi4ByYYwMAAKAmlRsEb9++Xa+88oo2b94swzBktVoVHR2tBg0aKCgoSLm5uUpNTdX+/fu1fv16ffPNN3r++ec1efJk3XHHHUxWAQC1jtPpUtLhdHfweyw2TblZDklSQH1vNelQXxGtC4LfoFCfOh1mmkwm+Qba5RtoV1jTsvsYLkNZ6XmnQ+Lk3BIri+NiUpSZlifDVTIsttrNpVcTF78N8Zbd21KnP7/A2TDHBgAAgCecNQh+/PHHtXz5cvn6+uqGG27Q8OHD1bVrV/n4+JTZ//Dhw/r222+1dOlSTZ8+XYsXL9YLL7ygnj17VlvxAACciyPfqcSDp9wXdzu2/5QcuU5JUnBDX7XsHuYOfgNCvD1cbe1TcBE6L/kFealhs8Ay+7hchrLS8srYr7jg9sjvKcpKy5VRMiuWzcviDoX963mVXFkcXLANhd2byxmgbmGODQAAAE85629X69at09SpUzVp0iR5e5/7F+MmTZro5ptv1s0336wdO3botdde0+bNm5mkAgBqVF6OQwn7Tyl+X6ri9qQo4eCpgn1wTVL9SH+169fIvdWDbyCr6qqC2Wxyr/w9G6fTVRgWF19ZnOPev/jk0QxlpedJZ4TFdh9rGauKS64sttkt1fwKgarDHBsAAACectYgeM2aNfL3L/sCNOfSqVMn/fe//1VGRsZ5FwYAQEXkZObreGya4gpX/CYdTpfhMmQymxTaJECdBzRWROtgNWoZJG8/m6fLvWRZLGYFhHgXrroOKrOP0+FSZuqZexWfvp90ON29f3NxXr5W94Xs3Be6K75/cT0vWQmLUUswxwYAAICnnDUIPt8JalWPAQBAcVmn8gq2edhXEPyejMuQDMlsNalhs0B1H9JEEa2DFd4iiG0FLjIWq1mBDXwU2KDst8hLBVt9nA6LS+9bnLD/lHIyS4fF3v62s68qLgyMLTZzdb48QBJzbAAAAHgOvyEDAGq19OQcd/B7bG+qUo5nSSq4GFl4iyD1GtFckW2CFdYsUFYbqz7rOqvNoqBQXwWF+p61jyPPedZVxekns3VsX6r7AoHF+QTYyrioXeF+xfW85FfPSxYLYTEAAACAi1Olg+BVq1bpk08+0a5du3Tq1CkFBgaqQ4cOuuGGGzR06NDqqBEAcIkwDENpSdkFF3UrDH9PnciRVLBXbKNWQWrbt5Ei2gQrtEkAoRzKZLVbFNzQV8ENzx4W5+U4Sm9DkZyjjNRcpSVlKy4mRXk5zpInmSTfQHuZF7UrCo79guwy83WJ88AcGwAAANWtwkGw0+nUww8/rC+//FJG4WW/fXx8lJycrO+++04bNmzQF198oZdfflkWCyuyAADnZrgMJR/PLAh9Cz8y0/IkFbyVP6J1sDoPLNjjt36kv8xmk4crRl1h97bKHm5VvXC/s/bJy3acsbL49G3KsUwd2ZWs/NySYbHJJPkGlXNxu3re8g2yn/Vrec/m49r0aawyknPlH+Klvte3VJve4VX62lG7MMcGAABATalwEPzf//5Xq1evVtu2bfXQQw+pV69e8vX1VVZWljZv3qzp06dr9erV6tChg+66667qrBkAcJFyuQydPJrhDn3j96a693P1C/ZSRJt6imgdrIjWwaoX7iuTieAXnmP3sSrEx6qQiLLDYsMwioXFpVcWn4zL0KEdJ+TId5U4z2w2yTfYXnI1cbCXTp3M1s5v4+V0FPTPSM7Vunm7JYkwuA5jjg0AAICaUuEg+JNPPlFERITmz58vH5/TF3Hx9fXVgAED1KtXL40cOVIff/wxk1QAgCTJ6XAp6XC64vakKH5vmo7Hprrfbh8Y6qNmXRooolVB8BvYwJvgFxcVk8kkL1+bvHxtqh9Z9sW7DMNQbpbj9EXtzti3OPFQug5sO+EOf8/kyHNp06exBMF1GHNsAAAA1JQKB8Hx8fG66aabSkxQi/Pz89PgwYO1YMGCKisOAHBxceQ5lXDglOIKV/sm7E9zr4as18hPrXuFK6J1kCJaBcu/nreHqwWqn8lkkrefTd5+NjWICiizj2EYysnI1wePbijzeEZybnWWCA9jjg0AAICaUuEgOCQkRGZz+Rc/MZvNCgkJueCiAAAXh7xsh47tT3Nf3C3h4Cm5nIZkkhpE+av9FREFWz20CpZPgN3T5QK1kslkkk+AXf4hXmWGvv4hXh6oCjWFOTYAAABqSoWD4GuuuUZr1qzRww8/LJvNVup4Xl6e1qxZo8GDB1e6iM2bN2vGjBn67bffZLFY1LVrVz388MNq3759pcfKyMjQyJEjFR8fr2HDhunVV1+t9BgAgLLlZOQrfl+q4vcVBL9Jh9NlGAV7noY2DVCXQQUXdmvUMkhevqX/rQBwdn2vb6l183bLkXd6mwir3ay+17f0YFWobtU5xwYAAACKq3AQ/NBDD2n79u2aPHmyHnnkEfXo0UMmk0mGYeinn37Syy+/rHr16mnq1KmVKmD9+vW67777FBAQoDFjxkiSVq5cqQkTJmjOnDnq3LlzpcZ76aWXlJqaWqlzAABly0zLda/2jd+XqpNxmZIki82s8OaB6jGsmSJaByu8eZBsXlzNHrgQRfsAb/o0VhnJufIP8VLf61uyP3AdV11zbAAAAOBMFQ6Cr7/+euXn5yspKUmTJk2SxWJRvXr1lJKSIqez4MI/oaGhGjVqVInzTCaTvv766zLHzMvL09NPPy0vLy8tWLBALVq0kCRNnDhRN9xwg6ZNm6YlS5ZU+MX8+OOPWrBggR5//HE999xzFT4PAFDg1MnsgtB3b6ri9qYqLTFbkmTzsqhRyyC16tlQEa2D1bBpoCy28t/KDKDy2vQOJ/i9xFTHHBsAAAAoS4WD4Li4uBKPHQ6HkpKSSrQlJiZW6sm///57HT9+XOPGjXOHwJLUsmVLjRw5UosWLdKuXbsqtEVETk6OnnrqKQ0ePFiDBw8mCAaAczAMQ2mJ2YWhb4ri96a69yf18rWqUatgdbgiUhGtgxXa2F9mC8EvAFS16phjAwAAAGWpcBC8e/fuKn/yrVu3SpL69u1b6ljfvn21aNEibd26tUJB8PTp03XixAn973//U35+fpXXCgAXO8Nl6GR8puILV/zG70tV9qk8SZJPoF0RrYLV7ZpgRbYJVkgjP5nMJg9XDAB1X3XMsQEAAICyVDgIrg6HDx+WJDVp0qTUsaK2oj7l2b59u2bPnq2nnnpKDRs21NGjR6u2UAC4CLmcLiUdyXAHv8f2pSo3yyFJ8q/npcbt6imiVbAi29RTUJiPTCaCXwAAAAAA6iqPBsEZGRmSJH9//1LHitrS09PLHSM/P19PPvmkunTpogkTJlR9kQBwkXDmu5Rw6JT74m7HYtOUn1uwv2RQmI9adAtVZOtgNWodrMD6Ph6uFgAAAAAA1KRKB8Fff/21Fi5cqJ07d+rUqVMKCAhQx44dNW7cOA0ePLg6aizXO++8owMHDmjZsmWsZgNwScnPder4gTR38Hv8wCk5812SpPqRfmrbJ1yNWgcronWw/IK8PFwtAKA8tW2ODQAAgLqnUkHw3//+dy1evFiGYUiSfHx8lJKSou+++04bNmzQH/7wBz3zzDMVHq9o1W/RyuDiitoCAgLOev7+/fs1c+ZM3X333WrVqlVlXgoAXHRysx06tq9gi4e4PalKOpQul8uQySSFNglQx6siFdEqWBGtguXtb/N0uQCACqrqOTYAAABQlgoHwcuWLdOiRYvUrFkzPfzww+rXr5/8/f2VkZGhTZs26ZVXXtHHH3+sHj16aPTo0RUas/g+wB06dChxrLz9g4vs379f+fn5evPNN/Xmm2+WOv7555/r888/16BBg/TWW29V8JUCQO2QnZGnY3vTFLc3RfF7U3XyaIYMQzJbTGrYLFBdr22iiNbBatQiSHYfj+70AwA4T9UxxwYAAADKUuHkYOHChWrQoIEWLVqkwMBAd7u/v7+uueYa9erVS8OHD9fChQsrPEnt2bOn3nvvPW3atEnXXXddiWObNm1y9zmbyMhI/eEPfyjVnpWVpc8//1yNGzdW79691b59+wrVAwCelJGSq/h9KYrfW7DdQ8qxTEmS1WZWwxZB6jm8uSJbB6th80BZ7RYPVwsAqArVMccGAAAAylLhIHjPnj0aPXp0iQlqcUFBQRoyZIiWLl1a4Sfv16+fwsPDtXz5ct16661q0aKFJCk2NlYrVqxQ+/bt3SFufn6+Dh8+LB8fH0VEREiS2rVrp2effbbUuEePHtXnn3+uTp06lXkcADzNMAydOpGj+L2pit+Xqvi9qTqVlC1Jsnlb1KhlsNr2CVdE62CFNgmQxWr2cMUAgOpQHXNsAAAAoCwVDoIdDoe8vb3L7ePt7S2n01nhJ7fb7Zo2bZqmTJmi8ePHa8SIEZKklStXyuVy6R//+Ie7b0JCgoYNG6ZevXppzpw5FX4OAKgNDMNQyvGsguB3b8E+vxkpuZIkbz+bIloHq/PVUYpoHaz6Uf4ym7n4JQBcCqpjjg0AAACUpcJBcOPGjfXdd9/p0UcfLfO4YRjauHGjoqKiKlXAgAEDNGvWLL355ptaunSpTCaTunfvrqlTp5baNxgAaqM9m49r06exykjOlX+Il/pe31KtLmuok3EZBaFv4arf7PR8SZJvkF2RrYML9vdtHayQcD+ZCH4B4JJUXXNsAAAA4EwVDoKHDBmiN998U4888ogee+wxhYWFuY+dOHFCL774omJiYjRlypRKF9GnTx/16dOn3D5RUVGKiYmp0HiV6QsAF2LP5uNaN2+3HHkuSVJGcq6+/nCX1s79Xc78gqu/BzbwVtMO9dWoMPwNCvWRyUTwCwCo3jk2AAAAUFyFg+A77rhDX3/9tT777DN99dVXatmypRo0aKATJ05o//79ys3NVXR0tO64447qrBcAapVNn8a6Q+AihiGZzGYNvi1aEa2DFRBS/lt+AQCXLubYAAAAqCkVvvqQr6+v5s2bpwkTJshisej333/Xd999p99//11ms1kTJkzQvHnz5OvrW531AkCtkXUqTxnJuWUec+Q6Fd07nBAYAFAu5tgAAACoKRVeESxJ/v7+evrpp/Xkk09q//79Sk9PV0BAgFq0aCGrtVJDAcBFyzAM7d50TBs/3nfWPv4hXjVYEQDgYsYcGwAAADXhnDNLl8ulzz77TL/++qskqVu3brruuuvUpk2bai8OAGqbtKQsrZ8Xo6O7U9SoVZCadW6gH1ceKLE9hNVuVt/rW3qwSgBAbcccGwAAADWt3CA4Ly9Pt912m37++WdJBavg5s2bpwULFuiDDz6QzWarkSIBwNOcTpd+/fqItqw8IIvFpKtuilaHyyNkMpvkH+SlTZ/GKiM5V/4hXup7fUu16R3u6ZIBALUUc2wAAAB4QrlB8Ny5c/XTTz+pXr16GjJkiAzD0JdffqmtW7dq3rx5uvXWW2uoTADwnMRDp7R2zm6dPJqhFl1DdcW4NvKvd3rrhza9wwl+AQAVxhwbAAAAnlBuEPzFF1/Iz89PS5cuVXh4Qchxzz33aMSIEVq1ahWTVAB1Wn6uU5uX79f2tUfkE2jXdfd0UotuoZ4uCwBwkWOODQAAAE8oNwg+cOCArrnmGvcEVZIiIiI0ePBgrVu3rtqLAwBPObzzpNZ/FKP0kznqcGWk+o5pKS8fLtgDALhwzLEBAADgCeWmGhkZGYqIiCjVHhkZqczMzGorCgA8JTs9TxsW79WeLQmqF+6rMY90V0TrYE+XBQCoQ5hjAwAAVL1lv8TpxdUxik/NVkSwjx4dEq3R3SI9XVatUm4QbBiGzGZzqXaLxSLDMKqtKACoaYZhKGbzcW1cvE95OQ71HN5MPYc2k8VW+mcgAAAXgjk2AABA1Vr2S5yeWLJD2flOSVJcaraeWLJDkgiDiznn+5xNJlNN1AEAHpOWlK1vPtqtI7+nKLxFkK6+OVr1I/w9XRYAoA5jjg0AAFB1Xly92x0CF8nOd+rF1TEEwcWcMwieMWOGZsyYUeaxdu3alWozmUzatWvXhVcGANXM5XRp25oj+nHFAZksJl05vo06Xhkpk5lfzgEA1Ys5NgAAQNX48WCy4lJzyjwWn5pdw9XUbucMgiv79jTezgbgYpB0OF1r5/yuE0cy1LxLA105vo3863l7uiwAwCWCOTYAAMCF+f3YKb20OkZrdifKbJJcZUyXIoJ9ar6wWqzcIHj37t01VQcA1Ij8PKe2rDigX9cckY+/TUPv7qgW3UJ5iy4AoMYwxwYAADh/R5Kz9MpXe7RsW5z8vaz669BoNfCz6+nlu0psD+Fjs+jRIdEerLT2OeeKYACoK47sStb6j3br1Ikctb8iQv3GtJSXr83TZQEAAAAAgHNISs/VjLV79dGWwzKbTLrnypa696oWCva1S5LsVoteXB2j+NRsRQT76NEh0ewPfAaCYAB1XnZGnjYu3qeYzccV3NBXYx7ppojW9TxdFgAAAAAAOIdTOfl6/9v9en/DAeU6XBp3WWM9OLC1woNKbu84ulskwe85nDUITk5OVkhIyAUNXhVjAMD5MgxDe7YkaMPivcrLcqjnsGbqcV1TWW0WT5cGALhEMccGAAComJx8p+b+cEhvrtunlKx8De/cSI9c00YtQv09XdpFy3y2A4MHD9brr7+u9PT0Sg/6ww8/aPz48froo48uqDgAOF+nTmRr5Ru/6utZuxQU6qM/PnmZeo9qQQgMAPAo5tgAAADlczhdWvTjEQ14ab3+9dnv6hgZpBX3X643b+pOCHyBzroieMyYMXrnnXf0wQcfaNCgQRo+fLi6d++u4ODgUn0Nw1BMTIy+++47LVu2TPv371fz5s111VVXVWftAFCKy+nS9nVHtXn5fplMJl0xro06XhUps5mLwQEAPI85NgAAQNkMw9Dqncf14uoYxSZlqkvjYL18Yxf1a9XA06XVGWcNgv/2t79p3LhxevXVV7Vq1Sp9/vnnkqTIyEiFhoYqKChIubm5Sk1N1aFDh5SdnS3DMBQeHq4nnnhCN910k6xWtiAGUHOSjqRr3ZzdSjqcrmad6uvKCdEKCPE+94kAANQQ5tgAAAClfb/vhJ5fHaNfj6SqZaifZt7cQ0M6NJTJxKKuqlTuLLJNmzZ6++23dfToUS1evFjff/+9du3apaNHj5boV79+fV111VUaOnSoBg8eLIuFt14DqDn5eU79uPKAtn19RN7+Nl17Zwe16hHGPxgAgFqJOTYAAECBHUfT9MLq3fpu7wk1CvLWCzd01tjukbJazrqbLS5AhZYTREVFaerUqZo6dapycnKUkJCglJQUeXt7q379+goNDa3uOgGgTEd2J2v9vBidSspWu/6N1G9sK3n72TxdFgAA58QcGwAAXKr2J2Xo5a/26LPtx1TP16anhrfTzX2aypvr+lSrSr+vzNvbW02bNlXTpk2rox4AqJCcjHxt/Hivdv9wXEFhPho9tZsio+t5uiwAAM4Lc2wAAHApOJ6Wo+lr9mrR1iPyspr14MBWuvPKFgr0ZkFXTWCDMQAXFcMwtPfHBG1YvFe5mQ71GNpUPYc1k9XOXw0BAAAAAKiNUrPy9PY3sfpw40G5DEOT+jTVnwa0UmiAl6dLu6QQBAO4aJw6ma1vPtqjwztPKqxpgEb9uZ0aRPl7uiwAAAAAAFCGrDyHZm08qJnfxCoj16ExXSM19Zo2ahzi6+nSLkkEwQBqPZfL0I51R/XD8v2SpMtvbK1OA6JkNnMxOAAAAAAAapt8p0sLfjyi19fsVVJ6rga3C9NfhkSrbXigp0u7pBEEA6jVThxN17o5u5V4KF1NO9bXlRPaKLC+j6fLAgAAAAAAZ3C5DK3ccUwvfxmjQyezdFmzenp7Ynf1bBbi6dIggmAAtZQjz6kfPzuobV8dlpefVdfe0UGteobJZGIVMAAAAAAAtYlhGPpmT5Je+CJGu46dUtvwAH1wa08NiOb3+NqEIBhArXN0d7LWz4tRWlK22vZrpP43tJK3H1cQBQAAAACgtvnpUIpe+GK3Nh9IVuMQH702rqtGdYlgO8daiCAYQK2Rk5mv7z/Zp9+/P6bAUB+NeqirGrfl7SMAAAAAANQ2exLS9eLqGH21K0EN/O365/UdNP6yJrJbzZ4uDWdBEAzA4wzD0L6fEvXdwj3KyXSo+5Amumx4c1ntFk+XBgAAAAAAijmakqVXv9qrJb8clb/dqr9c20a39W8uPy9ixtqO/0MAPCo9OUffzI/RoR0nFdY0QCMfbKvQxgGeLgsAAAAAABRzMiNXb66L1dwfDkkm6c7Lm2vK1a1Uz8/u6dJQQQTBADzC5TK0Y/1Rbf50vwzDUP8/tFLngY3ZQwgAAAAAgFokI9eh97/br/e+3a/sfKdu7NFYfx7cWhHBPp4uDZVEEAygxp2My9C6ubuVcOCUmnQI0VUTohXYgH9AAAAAAACoLXIdTs374bBmrNun5Mw8XdcxXI9cG61WYf6eLg3niSAYQI1x5Du19fOD+mX1Ydl9rbrm9vZqfVlDmUysAgYAAAAAoDZwugwt/SVOr361R3Gp2erXsr7+OrStujYO9nRpuEAEwQBqRNyeFK2fF6PUhCy17ROu/n9oLW9/m6fLAgAAAAAAKriQ+1e7EvTSlzHak5ChTpFBev6Gzrq8dQNPl4YqQhAMoFrlZOZr05J92rXxmAIbeGvUg13VuH2Ip8sCAAAAAACFNu8/qee/2K2fD6eqRQM/vTWxu67rGM47eOsYgmAA1cIwDMX+nKRvF+5RTka+ul3TRJeNbC6b3eLp0gAAAAAAgKSd8Wl6cXWM1sckqWGgl54b20k39oiS1WL2dGmoBgTBAKpcRkqOvpm/Rwe3n1BokwCNvL+LQpsEeLosAAAAAAAg6eCJTL3y1R4t/zVeQT42PXFdW93Sr5m8bSzeqssIggFUGcNl6Ldv47RpWawMp6F+N7RSl4FRMvOXRAAAAAAAPC7xVI5eX7tXC7YckdVi0p8GtNTdV7ZUkA/X8LkUEAQDqBIn4zO0fu5uHd9/So3b1dNVN7VVUKiPp8sCAAAAAOCSl5adr3e+idUHGw/I4TQ0oVcTPTCwlcICvT1dGmpQrQmCN2/erBkzZui3336TxWJR165d9fDDD6t9+/bnPHfXrl364osvtHHjRh09elRZWVmKiorSkCFDdPfdd8vX17cGXgFwaXLmu7R11UH9vPqQ7N5WDb61ndr0ZkN5AAAAAAA8LSffqdnfH9Rb62OVlp2v67tG6OFr2qhpfT9PlwYPqBVB8Pr163XfffcpICBAY8aMkSStXLlSEyZM0Jw5c9S5c+dyz582bZp27Nihbt266frrr5ckbdy4UW+//bbWrVunjz76SH5+fIEDVS1+b6rWzd2t1IQstendUJf/obV8AuyeLgsAAAAAgEuaw+nS4p+O6rWv9yjhVK6ujg7Vo0Oi1SEiyNOlwYM8HgTn5eXp6aeflpeXlxYsWKAWLVpIkiZOnKgbbrhB06ZN05IlS8od4/rrr9crr7yiqKgod5vD4dDUqVP15Zdfat68ebr77rur9XUAl5LcrHx9vzRWu76LV0B9b418oIuadKjv6bIAAAAAALikuVyGVv12XC9/GaP9JzLVvUmwpo/vpj4t+J0dtSAI/v7773X8+HGNGzfOHQJLUsuWLTVy5EgtWrRIu3btKneLiIkTJ5Zqs1qtuuOOO/Tll1/qp59+qpbagUtR7C+J+nbBHmWfylPXwY3Va2QL2by4qigAAAAAAJ703d4kvfBFjHbEpalNQ3+9N7mnBrcLY+tGuHk8CN66daskqW/fvqWO9e3bV4sWLdLWrVsrtFfwmazWgpdnsRBSARcqIyVX3y6I0YFfT6hBY38Nn9JZYU0DPV0WAAAAAACXtG1HUvXCF7v1fexJRQb76OUbu2h0t0hZzATAKMnjQfDhw4clSU2aNCl1rKitqE9lLVu2TJLUp0+f8ysOgAyXoZ3fxWnT0lg5nYb6jmmpLoMby2Ixe7o0AAAAAAAuWfsSM/TS6hh9sfO46vvZ9fTI9rqpdxN5WVkQibJ5PAjOyMiQJPn7+5c6VtSWnp5e6XG3bNmi+fPnq2nTpvrjH/94YUUCl6jk+Eytn7dbx2LTFNW2nq6eGK2gUF9PlwUAAAAAwCUrPjVb07/eq8U/HZGPzaKpg9vojiuay9/L4zEfark6+RUSGxurBx98UF5eXnrttdfk7e3t6ZKAi4oz36Wfvjion744JJu3RYNuaafoPuHsKwQAAAAAgIekZObprfX7NHvTIcmQbu3XXH8a0FL1/b08XRouEh4PgotW/RatDC6uqC0gIKDC4x05ckS33nqrcnJy9N57753X3sLApezYvlStm7tbKcez1Pqyhrr8xtbyDbR7uiwAAAAAAC5JmbkOfbDhgN79dr8y8xwa2z1KDw1urah6vGMXlePxILj4PsAdOnQocay8/YPLEhcXp8mTJys1NVUzZ87UZZddVrXFAnVYbrZDPyyN1W/fxsk/xEsj7u+iph3re7osAAAAAAAuSXkOl+ZvOaw31u7ViYw8Xdu+of4yJFptGlZ8wSRQnMeD4J49e+q9997Tpk2bdN1115U4tmnTJnefc0lISNCtt96qxMREvf766+rfv3+11AvURfu3Jenb+THKOpWnLgMbq9eo5rJ7e/zHAwAAAAAAlxyXy9Cnv8bpla/26Ehytno3D9G7k9uqe5N6ni4NFzmPJz39+vVTeHi4li9frltvvVUtWrSQVLDP74oVK9S+fXv39g75+fk6fPiwfHx8FBER4R4jOTlZt912m+Li4vTSSy9p0KBBHnktwMUmMzVX3y7co/2/JKl+pL+uu6+zGjYL9HRZAAAAAABccgzD0LqYRL3wRYx2H09X+0aBmn17J13ZugHX7EGV8HgQbLfbNW3aNE2ZMkXjx4/XiBEjJEkrV66Uy+XSP/7xD3ffhIQEDRs2TL169dKcOXPc7Q8++KBiY2PVoUMHxcbG6o033ijxHJGRkRo7dmzNvCDgImC4DO3cEK9NS2PldLjUZ3QLdb2miSwWs6dLAwAAAADgkvPjwWS98MVu/XgwRU3r++r1Cd00olMjmc0EwKg6Hg+CJWnAgAGaNWuW3nzzTS1dulQmk0ndu3fX1KlTS+0bXJb4+HhJ0s6dO7Vz585Sx3v16kUQDBRKOZ6pdXN369i+NEVGB+vqm9oquCEbzAMAAAAAUNN+P3ZKL62O0ZrdiQoN8NK/RnfUuMsay8ZCLVSDWhEES1KfPn3Up0+fcvtERUUpJiamVPvatWurqyygznA6XPp59SFtXXVQNrtFAye3Vdu+jXh7CQAAAAAANexIcpZe+WqPlm2Lk7+XVX8dGq1b+zWTr73WRHWog/jqAi4Bx/enad3c3UqOz1TrnmG6/I9t5Bto93RZAAAAAABcUpLSczVj7V59tOWwzCaT7rmype69qoWCffkdHdWPIBiow/KyHfphWax2fBsn/2AvDf9TZzXr1MDTZQEAAAAAcEk5lZOv97/dr/c3HFCuw6VxlzXWgwNbKzzI29Ol4RJCEAzUUQd+TdI38/coMy1Xna+OUu/rW8juzbc8AAAAAAA1JSffqbk/HNKb6/YpJStfIzo30sPXtFGLUH9Pl4ZLEKkQUMdkpuXqu4V7FftzoupH+mnoPR0V3jzI02UBAAAAAHDJcDhdWvJznF79eo+OpeXoitYN9NchbdUpit/P4TkEwUAdYRiGft94TN8v2SdHnku9r2+hbtc2kYUrjQIAAAAAUCMMw9Dqncf14uoYxSZlqkvjYL18Yxf1a8U2jfA8gmCgDkhNyNK6ubsVvzdVEa2DNeDmtgpu6OvpsgAAAAAAuGR8v++Enl8do1+PpKplqJ9m3txDQzo0lMlk8nRpgCSCYOCi5nS49MuXh7X184Oy2s0acHNbtevXSCYz/8gAAAAAAFATdhxN0wurd+u7vScUEeStF/7QWWO7RcrKO3RRyxAEAxep4wfStG7ObiXHZ6pl9zBdMa61/IK8PF0WAAAAAACXhP1JGXr5qz36bPsx1fO16anh7XRzn6bytlk8XRpQJoJg4CKTl+PQD5/u1471R+Uf7KVhUzqreWf2GgIAAAAAoCYcT8vR9DV7tWjrEXlZzXpwYCvdeWULBXrbPF0aUC6CYOAicnD7CX0zP0YZqbnqdFWU+lzfQnYfvo0BAAAAAKhuqVl5evubWH248aBchqFJfZrqTwNaKTSAd+fi4kCCBFwEsk7l6btFe7Rva6JCIvx0w10dFd4iyNNlAQAAAABQ52XlOTRr40HN/CZWGbkOjekaqanXtFHjEC7SjosLQTBQixmGod+/P6bvP9mn/Dyneo1sru5DmspiZcN5AAAAAACqU77TpYU/HtH0NXuVlJ6rwe3C9Jch0WobHujp0oDzQhAM1FKpCVla/9FuxcWkqlGrIA24ua3qhft5uiwAAAAAAOo0l8vQyh3H9PKXMTp0MkuXNauntyd2V89mIZ4uDbggBMFALeN0urTtq8P68bODsljNunpitNr3j5DJbPJ0aQAAAAAA1FmGYeibPUl64YsY7Tp2Sm3DA/TBrT01IDpMJhO/k+PiRxAM1CIJB05p3dzdOhmXoRbdQnXluDbyC2bTeQAAAAAAqtNPh1L0whe7tflAshqH+Oi1cV01qkuEzCzKQh1CEAzUAnk5Dm1ZfkDb1x2Rb6Bd193bSS26hnq6LAAAAABVbNkvcXpxdYziU7MVEeyjR4dEa3S3SE+XBVyy9iSk68XVMfpqV4Ia+Hvpn9d30PjLmsjOtXlQBxEEAx526LeT+uajGKUn56jjlZHqM6alvHz41gQAAADqmmW/xOmJJTuUne+UJMWlZuuJJTskiTAYqGFHU7L02td7teTno/KzW/WXa9votv7N5efF7+Oou/jqBjwk61SeNizeq70/JqheuK/G/qW7GrUK9nRZAAAAAKrJi6tj3CFwkex8p55b9btGdomQhbegA9XuZEau3lwXq7k/HJJM0p1XtNB9V7VUPT+7p0sDqh1BMFDDDMPQ7k3HtfGTvcrPceqyEc3VY0hTWWy87QQAAACoy+JTs8tsTziVq/Z//0ItQ/3VuqG/Wof5q1VYgFqF+atpfV/ZLPyuAFyojFyH3v9uv977dr+y8526sUdj/Xlwa0UE+3i6NKDGEAQDNSgtKUvr58Xo6O4UNWoZpKtvbquQRn6eLgsAAABADYgI9lFcGWFwsI9Nf+gRpb2JGdp6MEWfbot3H7NZTGrewE+twwLUMqwgJG7d0F/NG/jJy2qpyfKBi1Kuw6l5PxzWjHX7lJyZp+s6huuRa6PVKszf06UBNY4gGKgBLqdL274+oh9XHpDZYtJVN0Wrw+URMvHWLwAAAOCS8eiQ6BJ7BEuSj82iaaM6lNgjODPXodikDO1NyNC+wtud8Wla9dsxuYyCPmaT1Ky+X4lwuHVYgFqE+snXzq/6gNNlaOkvcXr1qz2KS81Wv5b19djQturSONjTpQEew78OQDVLPHRK6+bu1okjGWrRNVRXjGsj/3peni4LAAAAQA0rCntfXB2j+NRsRQT76NEh0aUuFOfnZVXnqGB1jgou0Z6T79T+pEztS8rQvoR07U3M0N7EDK3bnShHUUIsKaqeT2E4HKBWof5q1dBfrcL8Fehtq/bXCHiaYRj6aleCXvoyRnsSMtQpMkjP39BZl7du4OnSAI8jCAaqSX6uU5tX7Nf2NUfkE2jX0Hs6qmW3ME+XBQAAAMCDRneLLBX8VpS3zaL2EYFqHxFYoj3f6dKhk5nam5DhDof3JWZoY+xJ5Tlc7n7hgd5q3dC/2F7EAWod5s9FslBnbN5/Us9/sVs/H05ViwZ+emtid13XMVwmE+/GBSSCYKBaHN55Uus/ilH6yRx1uCJCfce0lJcvf30HAAAAUPVsFnPhxeUCdF2xdqfL0JHkLHcwvDcxXfsSM7Ro6xFl5Z3enqKBv71UONwqzF+hAV4EaLgo7IxP04urY7Q+Jknhgd76z9hO+kOPKFm50CJQAkEwUIWy0/O04eO92rM5QfXCfTXmke6KaB3s6bIAAAAAXIIsZpOaNfBTswZ+uqZ9Q3e7y2Xo2Kkc7U0oCIYLVhKn69Nt8UrPcbj7BXpb1brh6WC4VeF2ExFB3gTEqBUOnsjUK1/t0fJf4xXkY9MT17XVLf2aydvGhRSBshAEA1XAMAzt2XxcGxbvU16OQz2HN1PPoc1ksfHXRwAAAAC1i9lsUmSwjyKDfXR19Ont6wzDUFJ6bsH2EoV7EO9LzNBXuxK04Mcj7n5+dotahfkXXqguwH2xuqh6vrJwQWzUgMRTOXp97V4t2HJENotZfxrQUndf2VJBPrwTFygPQTBwgdKSsvXNR7t15PcUhbcI1NU3t1X9CH9PlwUAAAAAlWIymRQW6K2wQG/1b1XywlonM3K1LzFD+5IKVhDvS8zQxn0ntOTnOHcfL6tZLUL9C4LhwnC4VZi/mtb3k4236KMKpGXn651vYvXBxgNyOA1N6NVEDwxspbBAb0+XBlwUCIKB8+RyuvTrmqPasmK/TBaTrhzfRh2vjJSJv4ADAAAAqGPq+3upvr+XereoX6L9VE5+QUBcuL3E3sQM/Xw4Rct/jXf3sZpNat7AryAYDvVXq8LtJpo38OMt/KiQnHynZn9/UG+tj1Vadr6u7xqhh69po6b1/TxdGnBRIQgGzkPS4XStm7tbSYfT1axzA101oY386/EXSAAAAACXlkBvm7o3qafuTeqVaM/Kcyg2MdN9gbq9iRn6/Vi6vvjtuFxGQR+zSWpa36/YheoKtppoGeYnXztxBSSH06XFPx3Va1/vUcKpXF0dHapHh0SrQ0SQp0sDLkr8ZAUqIT/PqS0rDujXNUfk42/TkLs6qmX3UC6UAAAAAADF+Nqt6hQVpE5RJQO7nHynDpzIdIfD+xLTtTchQ9/sSVS+03D3iwz2OSMcLthmgj1gLw0ul6FVvx3Xy1/GaP+JTHVvEqzp47upzxkr0gFUDkEwUI49m49r06exykjOlU+ATYZhKCfDofaXR6jvmJby9mMSAgAAAAAV5W2zqF2jQLVrFFiiPd/p0qGTWe5guOhCdZtiTyrX4XL3axjopdZhAWpVGAwX7EUcoBA/e02/FFST7/Ym6YUvYrQjLk1tGvrrvck9NbhdGAuwgCpAEAycxZ7Nx7Vu3m458gomHdnp+ZKkHtc1VZ/rW3qyNAAXo+2LpDX/lNKOSkFR0qC/S53/6OmqAAAAagWbxewOd4d2PN3udBk6mpJVcIE694Xq0rVo6xFl5Tnd/er72dUyrPiF6grC4rAALwLEi8S2I6l64Yvd+j72pCKDffTyjV00ulukLFyHB6gyBMHAWWz6NNYdAhcXs/k4QTCAytm+SFrxoJSfXfA47UjBY4kwGAAAoBwWs0lN6/upaX0/DVZDd7thGIpPyynYYiLh9D7EK36N16kch7tfgLfVvb1E64b+7rA4IshHZgLGWmFfYoZeWh2jL3YeV30/u54e2V439W4iLysXEgSqGkEwcBYZybmVagdwiXLmS1knpcwTUtaJgtvi97NOSHu+lJxn/OzIz5aW3Sd9/7pk8ZKsXpLFfsZtYftZj9nP6FO8zX7GuN6n2yx2yWz2zOcLAACgCphMJkUG+ygy2EdXtQl1txuGoaSMXO0r3F6i6GJ1a3YnaOHWI+5+vnaLewVyq6KgOMxfjUN8WYFaQ+JTszX9671a/NMR+dgsmjq4je64orn8vYiqgOrCdxdwFv4hXmWGvv4hXh6oBkCNceSWH+pmniz5OCet7HFMZsknRPJrIMOZq7J+nTBcDpkCowpCYkeelJdRECo78wrqKHGbU3BbVcy2MgJm7zICZIJpAABw8TCZTAoL8FZYgLf6tWpQ4lhyZp72Fe49XBQQf7/vpJb8HOfuY7ea1aKBn1o3DHBvM9EqzF9N6/vJbmW+UhVSMvP01vp9mr3pkGRIt/VvrilXt1R9f37XBqobQTBwFn2vb1lij2BJstrN6su2EDjDsl/i9OLqGMWnZisi2EePDonW6G6Rni7romAYhlxGwd5vTpchh8tVeGu4204/dsnhMuRwGnIZp/s4nKfPdRnFHxfcGnlZsuUky5qbLFvOCdlzk2XPTZE9L1nevEv3tAABAABJREFUeSnuD5/8gg8vV1aZtTplUYYlSBmWYJ0yBynd3Fhp1o46FRikVFOgUk3BSlGAUhSoZAUqzfBTvmGWM9vQHOMORZpOlBoz3mig2xOmyGI2uT+sZpMsVpOsXiZZzGZZzSaZTYXtZsnL5JCXySFvk0N25Rc8lkN2U768jHzZlC+7Cm5tcshmFDy2Fh6zGvmyufJkMfJkU74sroI2iytPFiNfZldewf2cPFlcWTI782R25cnkypPZmSdT0WNnnkxnrnK+EATTkqQfl7+jxj+/qDAjSYmmUB3p/qguG3WPR2oBAKCuCfGzq1fzEPVqHlKi/VROvmITT1+gbm9CurYdSdGKX+Pdfaxmk5o18HOHwy0LVxG3CPWTt40tDCoiM9ehDzYc0Lvf7ldmnkNju0fpocGtFVXP19OlAZcMgmDgLNr0DpdUsFdwRnKu/EO81Pf6lu52QCoIgZ9YskPZ+QUXqohLzdYTS3ZIUrlhsKt42GkYcjrLDkEdZQSkrhLthQHpWcPRovFdZ5xTdshauv308f9n777jo6rSP45/7pT0RkJCCk0QQm9SBKRGRQUUQUWk6rrq2huWtSzrWnBX15+KZbFRpQoqVTAUBQELIBY6Ii2hEyCkTLm/PyYZMiRAgCSThO/79ZrXzJx75t5nMiGc+8y5zynU7srb9ylxeN/TqXEUtQ+3eY4/cZNQsok2jlKVo0QbR4k2jhFD3r1xlGiOUs37+BghRtHJyhzTxiEiOGSGs9OM4DC1OWw0I4MIjlgiOWJEctSSn/SN5IQlDJvVcjJhazWwGvnJW8vJNotBiGEQ7n1u4dVfbmGk/UNCjJMzek+YAbzquIVLqoae/PmYeH+OOQ43TrfrlM/dfcrPD1xuKy63BZfb7t3ucJ3rz/V8mQTgSUh77/MT1HnPgyxOgi1Ogg0nQRZPAjv/FljwRi6BhpMAt5NAhwN7rifJbc/bt9087nls5mLDgd3MS2rn3VtNB3az5GZMuy123JYATGsAZv69NQDTFoRpDfAkiwsklA27J4Fs2IIwbJ7nloL3PkntvMTzKYnpX7/9kqa/v02Q4QAD4tlP5E/P8gMoGSwiIlKKIoLstKxZhZY1q/i0n8h1sm1/Jpv3HctbpO44G9OP8dVv6eQPYy0G1IwO4dK48LwSE2GeWsSxYYSqxAEAuU43k77fwduLNnPgeC5XN6rG4z2SqV8t3N+hiVx09FdJ5Azqt4tX4rcccbtNHHlJMofLjSPv3unytHsf521zutzk5rU53W5y89oKbne4TrdPNw63icPpSZA6XCf3X3Cfa3ceKZR0y3K4eHTqWl6auz4vGer2TY66TcyyytOdhc8sVMPAas2feXoyuenTp8B9/i3Abi2y3fPcM6PV6pM0LXAcwyDEfZww1xHCnEcIdR0hxHGYEOcRghyHCc49TJDDM2M3MPcwATmHsLqLTva5rUE4g2JwBUfjCq6JOzgGR0hVjgTHYIZWhdCqGKFVsYRWxQiLxRoUTrTVQpzFgsWgVFeT7vjnYZ46Ck/YppJoHGSPGcO/nbfwU8RVLB98Wakc033K71xJfNlwpiR/0fvM/wIAnz5ZbpPjrrxjmYWPUfhLiVMT4KfG6Pb+WzPcTmxmDha3A1veLOeCiemAvNnTnraCz50nn+M4pa1wgjuAYwQYh7z7DCzwmvx+NsNxTp9ZE+DUGiLBRi41Vv8HlAgWEREpcyEBNpokRdIkKdKnPcfp4o8DmXmzh0+Wmli6aZ/PuUFSVLBPcthTjzicyGB7Wb8Vv3C7Tb78eQ+vL9zIzkNZtLskmtFDGtDqlIS7iJQdJYJFzqCyXfJv5iVcvIlTnyRnEYlTp+f+5PYCCVF3EYnT/Mduk1ynG6e7cOK08D5Pvi7/tSdfU+D1eQmf0mQYYLdYsFsNbFYLdmv+Y8Pz2GLBbvMkSO15bQ6XSdedPzHs93nEZh1hf3AUYxpdy5Ial3Flw2o+yVHbqQlSa8FL/k/OJvWWArAWSKTmt1sKJ2qLPoYFiwXfPlbjlDIDRsknP91uyDp8Sk3dA4UXU/M+Pwju0yTLAsIgJAZCq0JUTQhtdfJ5SFUIjYXQmLzHVbEEhBJQsu+mxAzvkczTM3L5MvcKb1uw3corPZJL7ZgWi4EFg4v9SkWzQDLcm3R2FU4iny6JXWjmuysvke0+fTkSbwLcZWK6cjGdORgF6jx7y2q4crzlNgxXLn/Z+XeO/hnMvnXhOE9YsYW4iGt2jLhahcuKiIiIiP8E2qw0iI+gQXyET7vD5WbHoRN5yeFj3lITK7cdJMd5suRgXHgg9ap5SkvUzU8Ux4VVmhq5pmmyeOM+/j1/IxvSj9E4MYKxdzSlc72qpTr5QkTOTongcqCyJRsri8/X7ObvC8ZixMwjNP4IRxxR/H3BtZjuIVzbLCEvYVkgcepNWLpxOE+dZVog6emdZeo7o9V35mrhma2OvNcWSpy6zNPus6ikbWmzWQokTvMTqQUSpzZrgccWg9BAW95rLARYLQVem/+6U5Kxxdin3eZJ2hbcl8/rbBbslvzXntzv+awO/OhfRzJ47XSCXJ5kZrWsIzy0djrRIQG80rdnSf94y57LCVmHipnUPQAnDoHpKnpfgZEnE7dRNSGxZYGkblXfxyExYA8u2/daivL/putvfdkz8r5UsVWAhPifw/6PrB8smC5PfWLnCRtpP0RyhHBq+Tk2EREROTu71ULdWE9ZCDh5ZanLbbL7cJanxIR3sbrjTPtxJ5m5J8fO0aEBebOG85PD4dSrFkZceGCFSaD+sP0Q/56/gR+2H6Z2TAhvD2hJz6YJWM7jXEtESl65SQSvWrWKUaNG8euvv2K1WmnRogWPPvoojRo1KvY+Jk+ezMSJE/nzzz8JDw+na9euPProo8TExJRi5BfmfOuLnknBxZfcpufmcpu43XhnNZ1Xe94lwfntbvepr/Ftd5lm3kys0/fPP1ax270x5N2b+BzvXNq9xzA9l+m7Tmk/bKwk5dBUbvvMQcxROBhxgE87T2b4V24emdayxH4HTmUY5M0+9SQ0bRYLAXkzVG1Ww5ss9bRbCLBZCD1rkjNvhquliMTpKclS3wTrySSpzWIQYMtLtOa3WY1Cs2TLeoBimiY4nZguF7hcmHk3T5sT0+kClxMz14XpdHr65LflPXa5nDhdp2x3u7zbyduP6Tq5Pf/xHT9Ox+ryndEa5HJwx+rPODShBkaAHcMegGG35z22YwTkPT/lsSUgAAo8Nux2z/OS/Jk6cz2J29MmdQ9AZv72/ZB1BDjNFwjBVU4mbmPqQs12BRK5pyR3Q2I8tVAvYvbItYRe+ibhmemEhsZjj3wIUCK4PPL+XcnNxZ2bi5nrwHTkYuYWvrm9jx2+2xxn2V6gX/4xsn4NwHC7fWNxWTi+IfI0kYqIiEhFYLUY1IwJoWZMCCkNq3nbTdMkLSPbmxjekleLeM66NDKyTp5jhAfauLTayeRwfrI4KSq43CRYN6Qf5T/zN5K6YR+x4YG82KcJ/dvUwG71zwK8IlK0cpEIXrJkCX/7298IDw/nxhtvBGD27NkMGDCA8ePH06xZs7Pu47XXXuODDz6gdu3aDBo0iL179zJz5kx++OEHpk2bRmRk+TyJ+s9XG71J4HxZDhePT/uZtxZt9k2++iRX85OWhdtL+er5UmMYYDE8NUMtFvLuPZevWwzPzVpEu2GYWC0usORisTgwrLlgOMDIwbDkgiUXbLmYRv4tx3NPLm4jB5NcXOTgJgfTe59Lyi8HuHu+myCnJ77Yo3D3fAdc+ylrmi/AZgR4bpYAAiwB2PNuAdZAAiyBBFgDCLQGEph3H2wLItAWSJAtkGBbIMH2IM+9LYhgeyCh9mBCA4IItgWdfK0tkEBrIAGWgCKTgaZpepOf+clQn8c+CU8nuN2YzlzfNpfLkyR1FZ0kPX0S1I3pcpLrcpGT3+bMT8SefHzy9afsy9t2avL2DPsq4j1yStKkrJ1ukqE18zh7X3yxRI5RVNLY+9hmw7AZGBbTczPcGIYTAycGDgwzF8PMwXBnYbiyPI8tJobVxLBw8nVWAyMoFCM0AiM4EiM0CSOsCUa1aIywGIzwaIzwWIzIahiRcRjhcRjBIZ7jWzS4O5s52+Yw4rsRZLuyAUjLTGPEdyMA6FmnEswcv0Cmy3WaBKvDJ6l6LglY0+E4pyTsqdtKrJC3YXj+vfrcTv47ttg9bZbw8EJJ4HyWjOMlE4uIiIiUK4ZhkBgVTGJUMJ3rx3rbTdPkwPFcNu875lOHeNGG/Uz9cZe3X7Dd6p097C0xUS2cmtEh53W14/nYeegE/124ic/X7iYs0MYT1yRze4dLCA6oAJdjiVyE/J4Izs3N5R//+AeBgYFMnjyZOnXqADBw4ED69evHiBEjmDFjxhn3sWnTJj766CPq16/P1KlTCQ72XE7cqVMnnnzySd59912efvrpUn8v52PPkSwe3TWJq3//EfMEGCGwoFFr/lt9AI0SIvKSnwWSoD5J0ZPtlrxFlyz5SdLTtuft49R2C959Wg3PrE5rMdsLxlOoPe91GG6c7lwc7mxy3NnkuLLJdWWT484ix5VFjiubbFc2Wc4sspxZnHCc8D4uzs1t5p08myZWN9hcYHWDtcC9zY13W4gRSKgRSIgRQJAZ4Lk37AQRQjCRBJl2mn69z5sEzhfkhL8sMNlmj8ftdOJ2OjCdObhdxzGdJ5Or3mRl3s1wubG4TSx5MeTf4zbJcYPTDSdMOJjXXrCPxTzZZnUbec9P7qtcsNsxrFYMqxVstjM+xmbFsOY9zttmCbCDtYjt+Y9t+X3zHxex3eLZl3d7odec8jg/nry2go+xWE+22QrEWsRrtlzdA2daWqEfiS0+nktmfIbpcHiTUj6P82cYFnyc68DMOoaZeQTzRAZm5lHMrKOYWcc9t+wTnlvOcczcbMycXMxMF6YLTLeB221guj2PTbeBaVpxuy3gNnC7wXQFgutss3Kz8m7pxf/8bbaTien8BNepM5ztnuQXBWY7G/YCCTF7wJlnTJ9mFnVx+roxcbgdJ28uh+/zU9qcbmfhPmd5zen6OF1OHG4Hvxz4hba/ZHPbEjPvCgP4tGsmz5vP88WWL7Bb7dgtBW6nPLdZbGftU2RbEc9thg2708TqNLE63eDze1lEEraIROwZE6yOs8+CdTt8t+E6TTmR82G3Y8n/PSjyZscSEowRGXna7UZAgOf3NCAg73fzDNtPveX9Ludvx2Yr9qz+zd1TcO7ZU/ifWEJCyf18REREpNwzDIPY8EBiwwPpULeqz7bDmbls2X/cmyDevO8YK7YdZMaa3d4+ATYLdaqG5iWJw/PqEYdRKyaUAFvJTOLYfyyHUYs28+n3O7AYBnd3rss9XeoQFXJxXwUoUt75PRH83XffkZ6eTv/+/b1JYIC6devSu3dvpk6dyu+//37GEhEzZ87E7XZzzz33eJPAAH369OG9997j888/Z/jw4dhsfn+7hTy3fxrt1vyI6fKcJJonIGXNj4QF2rjjtjEldyDTBNMNbheYbky3Exy5mE5PAorcXBw5WWTlHCM7+7jnlnOcnJwT5ORkkpObRWZuFrm5WThyc8jNzcbhyMHhyMXpyMHpzMXpcOByem5uhxO304mrwGzP/GSsxZ2XlHWdTMxazJPJ2zAXRJoQ4DKwm2BzGdjzk7gFk7t5yVCLy3NvuEyMYk/gOpF3O3dhOdAsr3wHABYwLAaGBc/MLwtgMfLaDLBaMAwrWA1Mi4FpIe/ewG01cAcYuC0Gbgu4LQYuC7i89+C0GGRbwFng5rCYOCwmTgNyLSYOw8RhgRyLG4dhkmNxk2uY5Bhucgw3LsP07s+dd3/ysYHLKKr95GOr1YbVasNms2Oz2bHbArDbArDa7XmzlgMItHpmRgcWnAltCSQgb1az5xbkfR5kC857HESALcjz3BpEoD2YQFswgXkzoy1G+Z5tGndTe3a99xkW58lEj9tmEndzB2xVqkDOMTiR6Sm34DjgueWXYSiqzq4zy/cA9rxblYC8cgsxEFKzQPmFmFPKMeQtoBYU5ZlmfwrTNAsnnx1neFxUEtvhwJ2bgys3B1duNs7sbNyOHFw5OZ77AslCt3d25lE44cR0OMDhAIcTw+GZ7W04nBhOFxanC4ujBBOCeVwGOK3gsOX9G7KevBVsc9gMnBZwndqe39/bZuCwgWmzem52K6bNBnYbpt2G3WbDnpf0zk9IJ/2YzS3fmgQWuMLgb3NMqh3OIrf5XsjN+7nkOjEdThwOJy6HixyH0/tzsTrd2Fxgz7vZnIUf21wmdu/jvO3OUx6X4BdIbouB22bBbbfizvtZuPN+Dnjv7RBoxwi3Y9hDfJKmloBALIGBWAICseY9tgUGYQ0MwhYYjC0wCFtQMLbAEGwBQVgCCyZci0j22u0VeoZ63CMPs+vZZ7DknLwU1B1oJ+6Rh/0XlIiIiJQrVUIDaBMaTZva0T7tx7IdbN2fyea9x7ylJn7edYQ5v6R5L3SyWQxqxYR4k8P5JSbqxoYRdJoVhk9d0+j+7nVJO5LNh8v+IMfppn+bGjzYvR7xkUGl/dZFpAT4PTP6448/AtC+fftC29q3b8/UqVP58ccfz5gI/umnn864j0mTJrFlyxYaNGhQQlGXnMt+XoXF5ZussbgM2n6/kvRbW2F6CvJiuk1Ml2cFck9ZCDwroOfV8nXn1c/1zAYE3HjKeroNcIORl4DNv7e6izc7KTDvdqHchgkWMC14sr4GeQlU01MSwmLmlXgAi2FiMQpetg5YyXsOhtUokGj1PPcmXK0FErA2i+ex1YJhtXhmdFoMDJvFkyiwWryzOg1bgT42G4bVwqaxG7CdKJxQcIS6aPpwczBMjPwfeFG3vKS75+YqlIw/2e72bCuyPe/DLao9fxZ0MTiBXMMg2zDINQxyCt4sRbRhkGsa5Lghx7SQ44Zcp0FOrm+/XMMg22JwpMDzU7c7L7C+rd00CTRNAkwIyrsPBALz7j3PjbzfVYNA0yAAg0AMAgwLQeQ/txJoGARiIdCwEGBY8tqsBBr5bTaCDCsBhpUAw4bVkveLZ7EU+AW0gHHyF3JZzjy+uiaYm77BO9NzemeDHllj6PniaHDlFv3GbMEnF0ULrQqxDTBDYnCGROMIicIRFIUjOBJnUASOwHAc1kAcpvPMs1Ed+3Ac2o3jwJlnsDrdzvOe5Zr/WqfVCcF4biVWecfARiBBhp1A00aw20ZQ3n0gVoLcVgLzb6aVQLeFgPyby8DuNghwGZ6kp9vwJkNtLrA5zbwvm0ysDpMAlxur043F6cbicmNxuL3JaMPhwjjhKT9iOJzgcHpmzTqckJtb4N+eG3Cc6Q2dUYALbv3WhG83FfPH4ykxQN7MafKSzWZewtUMtnkSsXkJWZfNittuwWXz3LJsFo7ZDFw2Iy+ZbeC0mDhsnmS3w2qSa4Vcq5tci0muxU2O1U1O3n224SLH4ibb4iLb4iTL4iS34O9k3u9LrrsEyhg48m4FdmVgFGvWc0nMni5yH8WccW232M/7C6xljS18da2FmxYV+HvS3UKPxhZUQERERETOJDzITosaUbSoEeXTnpXrYmv+DOK8UhOb9h1j4fq9uPLqShoG1IwOKVBiIpx6cWGsTzvKP2f9fsqaRr8C0KtZAo9eVZ86sWFl+j5F5ML4PRG8Y8cOAGrWrFloW35bfp/T+fPPPwkNDSU6OrrQtoL7KE4i2JV3eWp6+jlcFn0BDh87zRTWXNj92wnvrEyXFZxG4RmbTgu4reCyGQVmk57sY1oNDMMCVgOL1YLF4kl2WqwWrFYrFqsVq9WKzWbDarNhy5v1abXaCbDbsdvs2O0B2O2eerUB9kACAgIJsAdjs+XNeLPZMGyee6x5l4ZbbZ5EhS3/kn8b5CfVDCMvkWbNKwxc4HF+ks1yasItb8Y0p122qsTZ1l5F+io3RoFEvWk1iW9mZXe3N8ooirPITy4XlWzGzHteONlsxU2IaRJSVLKZ/P0UTE7n7Qd3gWO4i9iW334yLpfpucw+x+Ug18zF4XaSk5dczDVd5Lod5Lgd5LpdOExPm8PtJMftItd0evrk39wuHHmPc3BxwnSTYbrJxUWuaZJb8DFuXFzY7FKrCQHgSSTnPbZjEmiC3YQA0+QXawi5NSwsGuj72mXuSKZWjcVlseIwLDgteVV7DXCabpymOy+xmosj40+ch7fidDuLCuOCGRiekgAWG3bDc2+zeh5bLVZP4stq85QMsNgJtgQTbgk/mRCzecoJ2Kw2b5vNsHn3aTNsnvu85Fn+frxtBR8b9jPupyKshmy6XJ7EsNN5crZ0wccOh6eGdYFtac//47T7S/rPv08uDOhT/9mOYbd5S2xgtVaMn49p4sbt/eLA6XLiMPMe532R4HK7Tm7P/3Ih/950ektqOE2nt1SH011gP6fs0+nw3U+2O7vwPgvsx2mejKM0WAyLz+/9qb//3n8X+f9W8v79/bT3J3ISnCwaWHBGjpNfU1+neUDzUolVTi9/LOgqydIlpaysx7EiIlIxRAGtY6F1bBg09iRuc11udh3K4s+DmfxxMJPtB0+wbftOlq45gcN15rPumLAAnuocBzlH2LXrSKnHLyLFd7YxrN8TwcePe6b7hIUV/hYpv+3YsWNn3EdmZmaRSeBz2Ue+/fv3A54axX53momERTvTH+qKcwJTPhX42bqA79yQkuK3aKTsOIDsYvUs/G/MAaw8lzq7pSz33P6gSFkaPtzfEUgpyCGnxPa1m92kvKb/d/xl//791KpVy99hFEu5GseKiEiFZOHsVwUfB1LOvJSTiPjZ6cawfk8ElzdNmjRh4sSJxMbGYrVqlUsRERGRi5HL5WL//v00adLE36EUm8axIiIiIhe3s41h/Z4Izp+xmz8zuKD8tvDw8DPuIzQ0tMjXn8s+8gUFBdG6deti9RURERGRyquizATOp3GsiIiIiJxpDOv3pbXPVAf4TPWDC6pVqxaZmZkcOnTovPchIiIiIiIiIiIiUln5PRGcP2thxYoVhbblt51tZsNll112xn1ERUVx6aWXXmioIiIiIiIiIiIiIhWS3xPBHTp0ID4+ni+//JJt27Z527du3cqsWbNo1KgRjRo1AsDhcLB161b27Nnjs48bb7wRi8XC+++/T1ZWlrf9888/Z/v27fTp0webze9VMERERERERERERET8wjBN0/R3EIsXL+bee+8lPDycXr16ATB79myys7OZMGECzZo1A2DXrl2kpKTQtm1bxo8f77OP//znP3z44YfUrl2blJQU9u7dy7x586hevTrTpk0jMjKyzN+XiIiIiIiIiIiISHlQLhLBACtXruSdd97h119/xTAMWrVqxSOPPELjxo29fc6UCDZNkylTpjBx4kS2b99OeHg4Xbt25bHHHiMmJqas346IiIiIiIiIiIhIuVFuEsEiInLuNm/eTK9evWjVqhWTJk0qss+DDz7IV199xZgxY2jfvj0AGzZs4MMPP+T777/n0KFDhIeH06ZNG+69914aNGjg8/qDBw8ydepUli1bxp9//smRI0cIDw+nZcuW3HXXXbRo0aLQMZOTk0lKSuLLL7/k7bffZuHChezdu5d77rmHBx54oMR/DiIiIiJSsWgcKyJS9pQIFhGp4AYPHsz333/PrFmzqF+/vs+2vXv30r17d2rUqMH8+fMBmDNnDk8++SSGYdCtWzeqV69Oeno6CxcuxDAM3n//fTp06ODdx+LFi3nwwQdp164d1atXJzQ0lB07drB48WJM0+S9996jc+fOPsdNTk4mNjaWuLg4MjIy6NChA2FhYdSvX58bb7yx9H8oIiIiIlLuaRwrIlK2tIKaiEgFN3DgQL7//nsmTZrEP/7xD59t06ZNw+l0cuuttwKwY8cOnn76aWJiYpg4cSLVq1f39t20aRP9+/fnySefJDU1lYCAAABatGjBd999R3h4uM++N2zYwIABA3jllVcKDaAB9u/fz6WXXsqECRMICQkp6bctIiIiIhWcxrEiImXL4u8ARETkwlx55ZVUq1aNL7/8kszMTG+7y+Vi2rRpBAUF0bdvXwA+/fRTcnJyePLJJ30GzwD169fn5ptvZt++faxYscLbXqVKlUKDZ4AGDRrQrl07tm3bxp49e4qM7amnntLgWURERESKpHGsiEjZ0oxgEZEKzmaz0b9/f9566y1mz55N//79Ac+lcOnp6fTr14+IiAgAVq9eDcCaNWvYunVroX1t374dgK1bt9KlSxdv+5IlS5g8eTK//vorhw8fxul0+rxu7969JCYm+rQFBgaSnJxcYu9TRERERCoXjWNFRMqWEsEiIpVA//79ee+995g0aZJ3AJ2/6MaAAQO8/Y4cOQLAuHHjzri/EydOeB+PHTuWl19+mZCQEK644gqqV69OcHAwhmHw9ddfs2HDBnJzcwvtIyYmBsMwLvStiYiIiEglpnGsiEjZUSJYRKQSqFq1Kj169GD27NmsXbuW6Oholi9fTpMmTWjatKm3X/6lcd988w3VqlU7636dTiejRo0iKCiIGTNmcMkll/hsX7t27Wlfq8GziIiIiJyNxrEiImVHNYJFRCqJgQMHAp4ZFJMnT8Y0TW677TafPi1btgTgxx9/LNY+Dx8+zNGjR6lbt26hwfOJEyf47bffSiByEREREbmYaRwrIlI2lAgWEakkWrVqRaNGjZg3bx6fffYZkZGR9OzZ06fPoEGDCAgI4D//+Q8bNmwotA/TNPnxxx+9l8jFxMQQHBzM9u3b2bdvn7ef0+nklVde4fDhw6X7pkRERESk0tM4VkSkbKg0hIhIJXLbbbfx7LPPkpOTw9ChQwkKCvLZXrt2bV599VWefPJJbrzxRjp06ECdOnWwWCykpaWxbt060tLS+OGHHwgICMBisTB48GBGjx7NjTfeSI8ePTAMg1WrVnHo0CHatWvHqlWr/PRuRURERKSy0DhWRKT0KREsIlKJ9O7dmxdffJGcnBxuvfXWIvtcd911JCcnM2bMGFasWMH333+P3W4nNjaWVq1akZKSQlhYmLf/Qw89RHR0NNOmTWP69OmEh4fToUMHHn74Yd5+++2yemsiIiIiUolpHCsiUvoM0zRNfwchIiIlY/369fTp04f27dszZswYf4cjIiIiIlIsGseKiJQ+1QgWEalERo8eDcDgwYP9HImIiIiISPFpHCsiUvpUGkJEpILbuHEjS5YsYePGjcydO5cWLVrQvXt3f4clIiIiInJGGseKiJQtJYJFRCq43377jf/+97+EhobSrVs3/vGPf2AYhr/DEhERERE5I41jRUTKlmoEi4iIiIiIiIiIiFRyqhEsIiIiIiIiIiIiUskpESwiIiIiIiIiIiJSySkRLCIiIiIiIiIiIlLJKREsIiIiIiIiIiIiUskpESwiIiIiIiIiIiJSySkRLCIiIiIiIiIiIlLJKREsIiIiIiIiIiIiUskpESwiIiIiIiIiIiJSySkRLCIiIiIiIiIiIlLJKREsIiIiIiIiIiIiUskpESwiIiIiIiIiIiJSySkRLCIiIiIiIiIiIlLJKREsIiIiIiIiIiIiUskpESwiIiIiIiIiIiJSySkRLCIiIiIiIiIiIlLJKREsIiIiIiIiIiIiUskpESwiIiIiIiIiIiJSySkRLCIiIiIiIiIiIlLJKREsIiIiIiIiIiIiUskpESwiIiIiIiIiIiJSySkRLCIiIiIiIiIiIlLJKREsIiIiIiIiIiIiUskpESwiIiIiIiIiIiJSydn8HYCIiJSN5ORkAGJiYliwYAFhYWGF+jz44IN89dVXjBs3jnbt2nnbBw8ezPfff1+oXURERESktOWPY8/klVdeoW/fvt7nubm5TJo0iXnz5rF161ZOnDhBREQEVatWpUmTJlxxxRX07NmzNMMWESl3lAgWEbnIHDx4kPfee4/hw4f7OxQRERERkWK7//77T7utYcOG3sdZWVkMGTKEdevWERMTQ0pKCrGxseTm5rJlyxbmzZvHmjVrlAgWkYuOEsEiIheR4OBgwsPDGTduHLfeeis1atTwd0giIiIiIsXywAMPFKvf2LFjWbduHR07duT9998nICDAZ3tOTg4//vhjaYQoIlKuqUawiMhFxGaz8cgjj5Cbm8t//vMff4cjIiIiIlLiVq9eDcCAAQMKJYEBAgMD6dixY1mHJSLid0oEi4hcZPr06UPjxo356quvNBNCRERERCqdqKgoAP744w//BiIiUs6oNISIyEXGYrHw1FNPMXjwYF555RWmT5+OYRj+DktERERE5Izefvvt02676667CAwMBKBnz5588cUXvPXWW+zatYvOnTvTqFEjEhMTyypUEZFySYlgEZGLUNu2bbnqqqtYuHAhX3zxBX369PF3SCIiIiIiZzRq1KjTbhs6dKg3EdylSxeef/553nzzTaZMmcKUKVMAz0zhli1bcsMNN3DNNddoMoSIXHSUCBYRuUgNHz6cJUuW8N///pcePXoQHBzs75BERERERE5r48aNxe47cOBAbrrpJr777jvWrFnDhg0bWL16NYsXL2bx4sV07dqVUaNGYbfbSzFiEZHyRTWCRUQuUrVq1WLw4MHs3buXDz/80N/hiIiIiIiUqMDAQLp168ajjz7K6NGjWbVqFf/9738JCQlhyZIlTJ482d8hioiUKSWCRUQuYn/729+oUqUKH330EXv37vV3OCIiIiIipcZqtdKzZ0+GDh0KwPLly/0ckYhI2VIiWETkIhYREcEDDzxAVlYWr7/+ur/DEREREREpdSEhIf4OQUTEL5QIFhG5yPXv35+6devy5ZdfsmHDBn+HIyIiIiJyQSZNmsSaNWuK3LZv3z6mTZsGQJs2bcoyLBERv9NicSIiFzmbzcaTTz7JXXfdxZ9//nnGvqNHj2bmzJlFbrvyyiu58sorSyNEERERERHefvvt025r3rw5nTt3BuDbb79lxIgRJCYm0rJlSxITE3G5XOzatYulS5eSk5NDq1atGDRoUFmFLiJSLvg9ETxu3Dh++eUXfvnlF7Zv345pmqxbt47AwMBz3tfkyZOZOHEif/75J+Hh4XTt2pVHH32UmJiYUohcRKTy6NKlC1dccQXLli07Y78zbU9KSlIiWERERERKzahRo067bciQId5E8PDhw2nbti0rV67kt99+Y/HixTgcDiIjI2ndujXXXHMNN954I3a7vaxCFxEpFwzTNE1/BpCcnAx4EgjHjh3j6NGj55UIfu211/jggw+oXbs2KSkp7N27l3nz5lG9enWmTZtGZGRkaYQvIiIiIiIiIiIiUu75fUbw6NGjadq0KdHR0QwePJjvv//+nPexadMmPvroI+rXr8/UqVMJDg4GoFOnTjz55JO8++67PP300yUduoiIiIiIiIiIiEiF4PfF4rp06UJ0dPQF7WPmzJm43W7uuecebxIYoE+fPtSuXZvPP/8cp9N5oaGKiIiIiIiIiIiIVEh+TwSXhJ9++gmA9u3bF9rWvn17jhw5wpYtW8o6LBEREREREREREZFywe+lIUrCn3/+SWhoaJEzi2vWrAnAjh07aNCgwVn3lZ2dza+//kpsbCxWq7XEYxURERGR8s/lcrF//36aNGlCUFCQv8MpFo1jRURERC5uZxvDVopEcGZm5mnLS4SFhQFw7NixYu3r119/ZeDAgSUWm4iIiIhUXBMnTqR169b+DqNYNI4VERERETj9GLZSJIJLUmxsLOD5gcXHx/s5GhERERHxh/T0dAYOHOgdG1YEGseKiIiIXNzONoatFIng0NBQjh8/XuS2/Pbw8PBi7Sv/Mrr4+HiqV69eMgGKiIiISIVUkUosaBwrIiIiInD6MWylWCyuVq1aZGZmcujQoULbduzYAZysFSwiIiIiIiIiIiJysakUieDLLrsMgBUrVhTatmLFCqKiorj00kvLOiwRERERERERERGRcqFCJYIdDgdbt25lz549Pu033ngjFouF999/n6ysLG/7559/zvbt2+nTpw82W6WogiEiIiIiIiIiIiJyzvyeHR09ejTbtm0D8N4/++yz3loWTzzxBNHR0QDs3buX6667jrZt2zJ+/HjvPurXr88dd9zBhx9+SJ8+fUhJSWHv3r3MmzePWrVqce+995bxuxIREREREREREREpP/yeCP7222/5/vvvfdq+/PJL7+P777/fmwg+k8cff5waNWowceJExo8fT3h4OH369OGxxx4jMjKyxOMWERERERERERERqSj8ngguOLP3bKpXr87GjRuL3GYYBrfeeiu33nprSYUmUq4dPXqUffv24XA4/B2KiMg5sdvtxMXFERER4e9QREREpJzS+Y6ISGEXei7l90SwiJy7o0ePsnfvXpKSkggODsYwDH+HJCJSLKZpkpWVxe7duwGUDBYREZFCdL4jIlJYSZxLVajF4kTEY9++fSQlJRESEqJBkYhUKIZhEBISQlJSEvv27fN3OCIiIlIO6XxHRKSwkjiXUiJYpAJyOBwEBwf7OwwRkfMWHBysSz1FRESkSDrfERE5vQs5l1IiWKSC0jfjIlKR6W+YiIiInInGCiIiRbuQv49KBIuIiIiIiIiIiIhUclosTkT87uuvv+bTTz/lt99+IzMzkypVqtCyZUtuuukmOnfuDED37t3p0aMHTz75pJ+jlXxfffUVEydO5Pfffyc7O5ukpCS6du3KHXfcwfbt2xkyZAizZs2ifv36Pq9bvHgx99xzD6mpqVSvXt1P0VdeM2bMYMKECfzxxx/YbDaSkpJo164dTz/9tLdPcnKy93FgYCC1atXi1ltvZcCAAVgs+o5YREREpCS8/fbbjBo1yvs8NjaWli1bMnz4cGrWrAkUb+wGsGfPHkaNGsW3337L4cOHiYuLo3v37tx7771ER0d7++3atYuUlJQi42nfvj1jxowp+TdaSRXn8zsfEyZM4F//+hcbN24siTClnDnTeXK1atV46qmn+P7771m0aJFf4lMiWET86uWXX2b8+PH06dOHAQMGEBUVxe7du5k7dy5//etfWbhw4QX9JyulY+TIkYwdO5a+ffsybNgwwsLC2LJlC5MnT2bXrl0MGTLE3yFelP73v//x5ptvcuedd/LYY4+Rk5PDb7/9xpdfflnoZOKOO+6gR48eZGVlkZqaygsvvIBpmgwaNMhP0YuIiIhUPuHh4Xz44YcA7Ny5kzfffJNhw4Yxe/Zsxo8fX6yx2+bNmxk8eDAxMTE88sgjVK9enW3btvH++++zZMkSJk6cSLVq1XyO++STT9KqVatCsci5OdPnFxIS4ufopLw523nyO++8w/Hjxwv9ey1LSgSLiN98/fXXjB07lldeeYW+fft629u0aUOfPn1YtGgRgYGBfoxQirJo0SI++eQTXnrpJW666SZve9u2benfvz/Lli3zY3QXtwkTJtC/f38effRRb1v37t25//77C/VNSkqiRYsWgGd2yJYtW5g0aZISwSIiIiIlyGq1esdcLVq0ICEhgYEDB7J06dJijd1M02T48OFERkYyZcoUwsLCAM/Yu1u3blx//fX885//5N133/U57iWXXOI9rpy/M31+1157rX+Dk3KluOfJ69at44knnvBXmEoEi1zsPl+zm/98tZE9R7JIjApmeI9k+rRMKpNjjx07lqZNm/okgQvq3r17mcRREW1alc6KL7Zy/FAOYdGBtL+hLvXbxZfJsceMGUPjxo19/nPLZ7Va6dKlC6tWrSqTWMqrdevWkZqaSkZGBpGRkaSkpNCsWbNSP+6xY8eoWrVqofbiLCbQuHFjJk6cWBphiYiIiPiNP893itKkSRMAdu/eXayx2w8//MD69et59dVXvUngfNWqVWPw4MGMGjWKXbt2Vcqya3O2zeHN1W+SnplOfGg8D7V6iJ51evotnoKf36FDh3j11VdZsmQJ2dnZNGvWjCeeeIKmTZt6++fm5jJy5Ei+/PJLLBYLffr0ITExsdB+jxw5wuuvv05qairHjh2jcePGPP300zRv3tzbJzk5maeffpr09HRmzpwJwF133cVf/vIXZs6cyahRo8jIyOCqq65ixIgRPpOq1q9fz8iRI1m7di0BAQF07tyZp59+2uf373//+x/Tp08nPT2d8PBwGjZsyMiRI4mNjS3xn2Npypg1i31v/B/OtDRsCQnEPfIwkb17l/pxi3OenJ6ejmma9OjRw7vt6NGjvPrqqyxdupSMjAxiYmK44oorePHFF0slTiWCRS5in6/ZzdMzfiHL4QJg95Esnp7xC0CpD46cTidr167ljjvuKNXjVEabVqWzeOIGnLluAI4fymHxxA0ApZ4MdjgcrFmzRp/bGaxbt45Zs2bhcDgAyMjIYNasWQClngxu1KgREyZMIDExka5du1KlSpViv3b37t1FnoiIiIiIVFT+PN85nd27dwNQtWrVYo3dfvjhBwCuvPLKIvd35ZVX8vbbb/PTTz/5JILdbjdOp9Onr8ViqVDrQczZNocR340g25UNQFpmGiO+GwHgt2Rwwc/vvvvuY8eOHTzxxBNUqVKFjz76iCFDhvD5559Tq1YtAF577TWmTZvGI488Qt26dZk2bRrz58/32Wdubi633347R48e5YknniA6OppJkyYxbNgwFixY4JOI/fjjj+natSuvv/46S5Ys4d///jcHDx7kl19+4dlnn2XPnj288sorXHLJJdx1110AHDp0iMGDB1O3bl1ef/11MjMzef3117n99tv57LPPCAgI4PPPP+f999/n8ccfp169ehw5coSVK1eSlZVVRj/ZkpExaxZpzz2Pme35nXHu2UPac88DlGoyuLjnyfHx8Xz77bc+ba+88gpr1qzh73//O1WrViUtLY0ff/yx1GJVIlikkvjsp11M/XHnOb1mzY4j5LrcPm1ZDhdPTF/HpO93FHs/t7SuQb/Lzu3b5yNHjpCbm0tCQoJPu2mauFwu73Or1Vqs2YwV1YaVaaxfnnZOr9n7RwYup+nT5sx1s2j8en5btqfY+2nYMYEGlyecvWMBp/vcKqu1a9eyZs2ac3rNrl27fH6HwTMw+OKLL/jpp5+KvZ+WLVue8+V8zz//PPfddx9PPfUUhmFQt25drr76av7yl78UmkGSf3KQnZ3N119/zYIFCxg6dOg5HU9ERESkrFS0852C8hOyO3fuZMSIEYSGhtKhQwcaNGhw1rHb3r17iYiIKDSWy5c/u3Tv3r0+7ffee2+hvvfffz8PPPDAeb+PC/Hl1i+ZuXnmOb1m3f515LpzfdqyXdk8v/x5pm+aXuz93FjvRq6ve/05Hbugoj4/m83G6tWrGT9+PG3btgXg8ssvp3v37nz00Ue88MILHD58mMmTJ/PAAw94E4SdOnXiuuuu89n/F198webNm5k9eza1a9cGoEOHDlxzzTV8/PHHPgum16pVixdeeMHbZ/78+UybNo3Fixd7f0e+//57Fi5c6E0Ef/zxxwB89NFH3j61a9fmlltuYcGCBfTq1Yt169ZxxRVXMHDgQO+xrr766vP+mZWEI59/TsZnM87pNVk//4yZ6/s7Y2Znk/bMsxyZOq3Y+4ns15eoPn2K3f9CzpN/+eUXBg4c6PN7ccMNN5zzfopLieBywF+XEIucOig6W3tpODXJ+/HHH/Pvf//b+/y5555TzdJTnJoEPlt7aajMyfkLdWoS+GztJalBgwbMmzePZcuWsWzZMlauXMm7777L3LlzmTFjBqGhod6+L730Ei+99BLg+Tz79OlTZC1hERERkYqqPJzvHDlyhMaNG3ufJyYm8sYbbxAXF0dcXFyxx27n6umnn+ayyy7zaYuLizvv/fnDqUngs7WXhtN9fr/88gsxMTHeJDBASEgI3bp1807+2LRpEzk5OaSkpHj7WCwWUlJSvAvQAaxYsYLGjRtTvXp1n1ncbdq04ddff/WJp3379j77ql69OkFBQT5fFNSqVctnMsu6devo2LGjT5/mzZuTlJTETz/9RK9evWjYsCHTp0/nrbfeomvXrjRu3Bir1XpePzN/OjUJfLb2knY+58kNGjTgo48+wmKx0KFDBy655JJSiOwkJYL9zJ+XEEvl0u+y6uf8LXXHkYvYfaTwpR5JUcFMubt9Ea8oOVFRUQQEBJCenu7TfsMNN3j/My2qtk5l0+Dyc5+VO/bvyzl+KKdQe1h0IDc+1qqIV5Sc/M9tz54zzzzOHzS43YUH2fkJUZut/P8X1KJFi3OelfvGG2+QkZFRqD0yMpLbb7+9hCI7vYCAALp37+6tsT1t2jSeffZZpk+f7jPj9y9/+QvXXnstQUFB1KhRg6CgoFKPTUREROR8VbTznXzh4eF88sknGIZBbGwscXFxPsmis43dqlWrxtGjRzl+/HiRs4Lzx+XVqlXzaa9Vq5ZPrVp/u77u9ec8K/fq6VeTlln46smE0AQ+ueaTkgrtjE73+S1atIiYmJhC/WNiYrznAgcOHPC2ndqnoMOHD7N27VqfhHO+mjVr+jyPiIjweW6324tsy8k5eb64f/9+6tWrV2jfVatW9cbar18/MjMzmTJlCu+88w5RUVHceuutPPjgg35LCEf16XNOs3IBNndPwVnEuaotMZFa48eVUGSFFfc8uSjPP/88b731Fu+++y4vvPACtWrV4qGHHqJnz9Ipf1JxisNUUqmpqd4kcD6Hw0FqaqqfIpKLyfAeyQTbff+oB9utDO+RXOrHttlstGjRguXLl/u0V61alaZNm5arQUt50/6GutgCfP982wIstL+hbqkf226306pVK++Kp6cTHR0NeAYdp9q/fz8Wi4WoqKjSCNHvUlJSsNvtPm12u91nJkBZuvnmm4mKimLbtm0+7YmJiTRt2pR69eopCSwiIiKVkj/Pd/JZrVaaNm1KkyZNqFat2llnDJ46dmvTpg3AaXMEqampGIZB69atSzbwcuChVg8RZPUdpwZZg3io1UNlFsPpPr/Y2FgOHjxYqP/BgweJjIwE8K6/cWq/U59HRkbSpEkTpk+fXug2atSoC34Pp4v1wIED3lgtFgvDhg1j3rx5LFmyhL/85S+MHj2aqVOnXvDxy1LcIw9jnHJuYwQFEffIw6V63OKeJxclIiKCZ599luXLl/PFF1/QrFkzHn/8cbZs2VIKkSoR7HdFzRo7U7tISerTMolX+jYlKSoYA88346/0bVpmCycMHTqUn3/+mc8//7xMjldZ1G8XT7eBDQiL9qwCGxYdSLeBDUp9obh8Q4cO5ddff/WuVFuQ2+3mm2++oXbt2sTGxhY5YE1NTaVJkyaVNvnYrFkzevfu7R1URUZG0rt37zK5yqOoAd6hQ4dOuyK1iIiISGXm7/OdsynO2K1NmzY0bNiQd999l8zMTJ+++/btY9y4caSkpJCUVD7eU0nqWacnIzqMICE0AQODhNAERnQY4beF4gpq3rw5Bw8e9C7mB5CVlcWSJUu8JTnq169PYGCgzzmR2+0udI7Uvn17duzY4Z2oUfCWnHzhX1o0b96cZcuWcfz4cW/bunXr2L17d6HyIQAJCQncdddd1KxZk61bt17w8ctSZO/eJPzrBWyJiWAY2BITSfjXC6W6UFy+4pwnn02DBg144okncLvdhSbylJTyf11uJRcZGXnaS4hFykKflkl+GwhdeeWVDB06lKeffppVq1bRvXt3qlSpwpEjR7zfpIWEhHj7//HHH4VWWA0ODqZLly5lGnd5UL9dfJklfk/VvXt3br/9dp555hlWr15NSkoKISEhbNu2jcmTJ5OUlETnzp257777+Oc//4lhGHTt2hWHw8Hs2bNZvnw577//vl9iLyvNmjXzS3mf3r17k5KSQseOHYmJiWH37t18/PHHBAUF0eccL6sSERERqQz8eb5zNsUZuxmGwb///W+GDBlC//79ufPOO0lKSmLbtm28//77hIeH8/zzzxfa9x9//EGVKlV82gIDA2nYsGFZvLUS07NOz3KR+D1Vp06daNmyJY888giPPfYYUVFRfPzxx2RnZ/OXv/wFgCpVqnDLLbfw9ttvY7PZuPTSS5k2bRonTpzw2VefPn2YPHkygwcP5o477qBGjRocOXKEdevWERsby7Bhwy4o1ttvv51JkyZx5513cuedd3LixAlef/116tev710Q7vnnnycyMpLmzZsTHh7OqlWr+PPPPxk+fPgFHdsfInv3LpPE76mKe558qgEDBnDVVVdRr149DMNg6tSphISElNr5pBLBfpaSkuJTIxg8lx746xJikbL297//nTZt2vDpp5/yzDPPkJmZSZUqVWjZsiWjR4/2SfIuXryYxYsX+7w+KSmJRYsWlXXYF72nnnqKli1bMmHCBB577DFycnJISkqie/fu3hVxBwwYQEhICGPHjmX69OlYrVYaNmzI+++/f1Em78vCfffdR2pqKi+++CIZGRnExsbSsmVL3njjDWrUqOHv8ERERESkgOKO3erXr8+MGTMYNWoUr732GkeOHCE2NpaUlBTuvfdeb1m2gl599dVCbTVr1mThwoWl+p4uJu+++y4jR47k5ZdfJicnh2bNmjF27Fhq1arl7fPEE0/gdDp55513sFgsXH/99dx+++2MHDnS2ycwMJBx48bx5ptv8vbbb3Pw4EGio6Np1qyZt3b0hYiOjmbcuHGMHDmSxx57DLvdTpcuXXj66acJCAgAPGujTJ06lSlTppCTk0PNmjX517/+xZVXXnnBx7+YFOc8+VQtWrRg5syZ7Nq1y3vO/MEHHxAfXzoTvwzTNMtumfkKYNeuXaSkpJCamkr16udWiP58rVu3jtTUVDIyMrxFuB988EHNCpbTWr9+fYX7JldE5FT6WyblmT/GhBeqIsYsIlIUjRFERM7sdH8nzzYe1IzgcqDgJcQHDx7kvffeY9asWQwcOPCsheRFREREREREREREzkaLxZUzMTExXHnllWzZsoW1a9f6OxwRERERERERERGpBJQILofatm1LzZo1mT9/fpELyYmIiIiIiIiIiIicCyWCyyGLxcINN9yAy+Vi1qxZqIyziIiIiIiIiIiIXAglgssplYgQERERERERERGRkqJEcDlWsETE0aNH/R2OiIiIiIiIiIiIVFBKBJdjBUtEfPnllyoRISIiIiIiIiIiIudFieByTiUiRERERERERERE5EIpEVwBqESEiIiIiIiIiIiIXAglgisAlYgQERERERERERGRC6FEcAWhEhFSGb399tskJyd7b1dccQUPPPAAO3bsuKD9Ll68mOTkZHbt2lVo24wZM0hOTubxxx+/oGNcrAp+Zg0aNKBNmzb069ePN954g/379xfqv3HjRu6++24uu+wyWrZsyU033cSvv/7q08fpdDJ69GiuvvpqmjRpQufOnXn55ZfL6i1VKmf6N3XPPffQu3fv0772hRdeoHXr1owePZpGjRpx8ODBIvvNnz+f5ORkfv7559J6GyIiIiKVwtnOd0rrfEhE5HRs/g5Aiq9t27b8/vvvzJ8/n7p16xIREeHvkEQuWHh4OB9++CEAO3fu5M0332TYsGHMnj2bkJCQEj/enDlzAEhNTSU7O5ugoKASP0ZlV/AzO3bsGL///juTJk1iypQpfPjhhzRp0gSA9evXc9ttt5GSksIbb7wBwC+//EJ2drbP/p566ilWrlzJ/fffT506dUhLS2Pr1q1l+6YqkdP9m3r44YcZPnw4W7Zs4dJLL/V5jcvl4quvvuKqq67i+uuv57///S/z589n4MCBhfY/d+5catSoQfPmzcvk/YiIiIhUZGc63znb9tI4HxKRi5sSwRVIfomI9957j1mzZnHbbbdhGIa/wxK5IFarlRYtWgDQokULEhISGDhwIEuXLuXaa68t0WMdPHiQFStW0L59e1asWMGiRYu47rrrSvQYF4OCnxlAp06dGDBgAAMHDuTRRx9l3rx5WK1W/vGPf9C9e3dee+01b9/OnTv77Oubb75h3rx5fPHFF4WSk3J+TvdvyuVyERwczOzZs3n44Yd9XrNq1SoOHDhAr169iI+Pp3Xr1syZM6dQIvj48eMsXbqUYcOGlc2bEREREangznS+c7btJX0+JCKi0hAVTH6JiM2bN6tEhJSMdVPhjSYwIspzv26qX8PJn026e/dumjRpUmiBxM2bN5OcnMx3330HgGmavP3227Rv356WLVvyxBNPcPz48SL3PX/+fFwuF8899xzVqlXzzg6uiNZ/u5jR993O67f2ZvR9t7P+28V+jSciIoLhw4fz559/snz5crZs2cLPP//MoEGDzvi6zz77jMsvv7zSJYHT0r9g+fJOpC66lOXLO5GW/oXfYsn/N3Xw4EG6devGvHnzCvWZM2cOMTExXH755QD07NmT1atXk56e7tMvfyZ9z549Sz9wERERkZJQjs93zme7iMiFUCK4Amrbti01a9Zk/vz5hZJkIudk3VSY9SBk7ARMz/2sB/06OMof8FStWhWAhQsX+myfO3cuVatWpV27dgCMGzeOd955h1tuuYW33nqLoKAg/vOf/xS579mzZ9OoUSPq1q3LddddxzfffMOxY8dK8d2UjvXfLmbB6FEcO7AfTJNjB/azYPQovyeD27Vrh81m4+eff/bWjz169CjXX389jRo14sorr2TatGk+r1m3bh21a9fmhRdeoFWrVjRv3pz777+fvXv3+uMtlIi09C/YsOEZsnP2ACbZOXvYsOEZvyWDC/6b6tmzJ9u3b/ep0+xwOFi4cCHXXnstVqsVgB49emC1Wpk7d67PvubOnUv9+vWpX79+2b0BERERkfNVAc53znW7iMiFUGmICkglIqRIayfBmgnn9ppdP4Arx7fNkQVf3A8/jS3+floOghYDzu3YBTidTsBTE2vEiBGEhobSoUMHOnXqxNy5c+nXr5+379y5c71JKpfLxQcffED//v155JFHAE+Zgttvv71QInHPnj2sWbOGxx57DPDMePzkk09YsGCBz/7L2m9LU/l1ycKzdywgbdNGXE6HT5szN4ev3n+LdYu+KvZ+mnS9isZdUs7p2GcSGBhIlSpVOHDgAAEBAQA88cQT3HnnnTRt2pSvvvqKZ599lri4OLp06QLA/v37mTFjBg0aNOCNN94gMzOT//znP9x///1MnTrV73/b0tJmsCdt+jm9JiNjDaaZ69Pmdmexfv1T7Nkzpdj7SUy4iYSEvud07Hyn+zcVFRVFREQEc+bM8c42WbZsGRkZGT6zfKOjo+nQoQNz5szhjjvuyHtfGSxfvpz777//vGISERERuSCV8HxnypQpZ9wuIlLSlAiuoPJLRMyfP5+1a9fSsmVLf4ckFdGpg6KztZeCI0eO0LhxY+/zxMRE3njjDeLi4rjuuut46qmnOHz4MFWqVGH9+vVs376dl156CYC0tDT2799PSopvMvOqq67ylo7Il18GIr8mcNOmTalVqxZz5szxayL4fJyaBD5be1kyTdPn/uabb+avf/0rAJdffjlbt27lf//7nzcRnO/dd9+lSpUqAMTGxjJo0CBWrlxJ+/btyzD6knFqEvhs7SXtTP+mwPPvY/78+TzxxBMYhsHcuXNJSkoq9P9Iz549efLJJ9m5cyc1atRgwYIFOBwOlYUQERGRiqOcn+8UZ7uISElSIrgCa9u2Lb///jvz58+nbt26RERE+Dsk8acWA879W+o3muRdJnWKyBpwe9nUzw0PD+eTTz7BMAxiY2OJi4vzzgLt3r07NpuNBQsW0L9/f+bOnUt8fDyXXXYZAAcOHAA8X4wUdOpz8CSCGzVqRHh4uLekSvfu3Rk3bhwHDhzw26VXjbuknPOs3NH33e4pC3GK8Kqx9P/HyJIK7Zzl5ORw5MgRqlat6v17lF/CI9/ll1/O2LEnZ19ERERQo0YNbxIY4LLLLsNut7Nlyxa/J4ITEvqe86zc5cs75ZWF8BUUmMhlrT4tqdBO60z/pgB69erFZ599xpo1a2jcuDGpqalFXlly5ZVXEhgYyJw5c7jnnnuYO3cuzZs3p0aNGqX+HkREREQKqYTnO8XZLiJSklQjuALLLxHhcrmYNWuWdwaeSLGlPA/2YN82e7CnvYxYrVaaNm1KkyZNqFatms+gJzQ0lC5dunjrlM6bN49rrrnG2yc/eXvw4EGffZ76fOvWraxfv57ffvuNNm3aeG+ffPIJLpeL+fPnl+ZbLHGdbh2CLSDQp80WEEinW4f4KSKPlStX4nQ6adGiBXXr1gUo9HfJNE2fz7hu3bqn/dtlsVTM/6Lq1H0ci8X335XFEkyduo+XyfHP9G8KPMn5qlWrMnfuXJYsWUJmZia9evUqtJ+wsDC6du3KnDlzOHjwIKtWrdJsYBEREalYyvn5TnG2i4iUpIp5li1e+SUiNm/ezNq1a/0djlQ0zW6B3m95vhHH8Nz3fsvTXk707NmTH374gUWLFrFz506fRFRCQgKxsbGkpqb6vObUBebmzJmD1Wpl9OjRjBs3zueWnJzM7Nmzy+S9lJSGnbpx9V33E141FgyD8KqxXH3X/TTs1M1vMR09epTXXnuNWrVq0aFDB1q2bElkZCQrV6706bdy5UoaNGjgfd61a1c2bdrEoUOHvG0//PADDoeD5OTkMou/JCXE30CDBi8RFJgIGAQFJtKgwUskxN/g79AAz8nGtddey/z585k9ezZ169b1+UwK6tWrF5s2beKdd97BNE2uvfbaMo5WRERE5AJUgPMdEZGypNIQlYBKRMgFaXZLuR4IdenShaCgIJ5//nmqV69Os2bNvNusVit33nknr776KlWqVKF169YsWLCArVu3+uxjzpw5dOjQoVBdWoAbb7yRV199ld27d5OUlFTq76ekNOzUzW+JX5fL5f3iKTMzk99++41JkyaRlZXFhx9+iNVqxWq1cu+99/Laa68RERHhXSzuhx9+YMKEk4t89O/fn/Hjx/O3v/2Nu+++m8zMTF577TU6dOhA69at/fL+SkJC/A3lJvFblJ49ezJ+/HgWLlzIAw88cNp+Xbp0ISwsjE8//ZR27dqpVp2IiIhUPOX8fEdEpCxpRnAloBIRUpkFBQXRvXt39u/f713oraChQ4dy9913M3nyZB544AEyMzMZPny4d/uvv/7K9u3bueGGopNyPXv2xGKxeBeTk7M7duwY/fv359Zbb+Whhx5i/vz5XH/99cyaNYsmTZp4+w0bNozHHnuMqVOncvfdd7NmzRreeustnwRvWFgYY8eOJSIigkcffZQXXniB9u3b83//939+eGcXj5YtW5KUlIRpmkWWhcgXGBjIVVddddZ+IiIiIiIiUv4ZZjnJGq5atYpRo0bx66+/YrVaadGiBY8++iiNGjUq1usPHjzI//73P7799lv27NlDeHg49erVY9iwYUXOAjydXbt2kZKSQmpqKtWrVz/ft+MXK1euZP78+dxwww2FVn+XymX9+vU0bNjQ32GIiFwQ/S2T8qwijgkrYswiIkXRGEFE5MxO93fybOPBcjEjeMmSJQwbNoyNGzdy4403cv3117Nu3ToGDBjAunXrzvr6AwcO0KdPH8aNG0f16tUZNGgQXbp04ZdffuGuu+5i/PjxZfAu/K9t27bUrFmT+fPnc/ToUX+HIyIiIiIiIiIiIuWE3xPBubm5/OMf/yAwMJDJkyfz/PPP8/zzzzNp0iQMw2DEiBFn3cekSZPYt28fjz/+OB988AHDhw/npZdeYubMmQQHB/P++++X/hspB1QiQkRERERERERERIri90Twd999R3p6Otdffz116tTxttetW5fevXvz22+/8fvvv59xH7t37wagc+fOPu01atSgTp06ZGRkXDRJ0ZiYGK688ko2b97Mzz//7O9wREREREREREREpBzweyL4xx9/BKB9+/aFtuW35fc5nfwE8jfffOPTvnPnTrZt20br1q0xDKMkwq0Q8ktEzJs3TyUiREREKpC09C9YvrwTqYsuZfnyTqSlf+HvkEREREREpJKw+TuAHTt2AFCzZs1C2/Lb8vuczm233casWbN47bXXWLVqFcnJyRw+fJivvvqKSy+9lJdeeqnkAy/H8ktEvPfee8yaNYvbbrvtokqEi4iIVERp6V+wYcMzuN1ZAGTn7GHDhmcASIi/wZ+hiYiIlDnTNHUeKyJShAupeuD3GcHHjx8HICwsrNC2/LZjx46dcR9hYWF8+umndO3alW+++YYPPviA6dOnY7Va6du3L4mJiSUfeDmnEhEiIiIVh9N5jE2b/ulNAudzu7PYtvU1P0UlIiLiH3a7naysrLN3FBG5CGVlZWG328/rtX5PBJeEffv2MXjwYP7880/GjBnD6tWrWbx4MX379uWf//xnsRacq4xUIkJERKT8Mk03hw+v5LffHuPbZe1wOjOK7Jedk1bGkYmIiPhXXFwcu3fv5sSJExfNej8iImdjmiYnTpxg9+7dxMXFndc+/F4aIn/Wb/7M4ILy28LDw8+4j5EjR7J+/Xpmz55NvXr1AAgNDeXJJ59k27ZtTJkyhdtvv53atWuXbPDlnEpEiIiIlD/Z2Wmkpc8gbc90srJ3YLOFk5BwE/v3LyA3d3+h/kGBCX6IUkRExH8iIiIA2LNnDw6Hw8/RiIiUH3a7nWrVqnn/Tp4rvyeCC9YBbty4sc+2M9UPLmjZsmVERUV5k8AFtWnThiVLlrBhw4aLLhEMJ0tEzJ8/n59//pkWLVr4OyQREZGLjtudy/4DqaSlTePgwW8BN1WiLqdOnYeJjb0aqzWYyMjLfGoEA1gswdSp+7j/AhcREfGTiIiI8050iIhI0fyeCG7dujUffPABK1as4Nprr/XZtmLFCm+fM3E4HN7bqTUyDh8+DHDetTMqg7Zt2/L7778zb9486tSpo/9MpdyYMWMGEyZM4I8//sBms5GUlES7du14+umnffodOHCADz74gCVLlrBnzx5sNhuXXHIJ3bp1Y+DAgURHR3v3V/C10dHRNGrUiEcffbTQF01y7u655x52797NrFmzitz+wgsvMHPmTE6cOMF3331HTExMoT7z58/noYceYurUqTRv3pzk5GTvtsDAQKKjo2nSpAl9+/ale/fupfZeKqu3336bUaNGeZ/HxsbSsmVLhg8fftYvVc9k8eLF3HPPPaSmplK9enUAfXbFdPz4RvakTSc9/XMcjkMEBsZTu/bfSEy4ieBg388kf0G4bVtfIzsnjaDABOrUfVwLxYmIiIiISInwe43gDh06EB8fz5dffsm2bdu87Vu3bmXWrFk0atSIRo0aAZ6E79atW9mzZ4/PPlq0aIHD4WD06NE+7Xv37mXGjBnY7XZatmxZ+m+mnMovEeFyuZg1a5ZqLEm58L///Y9nn32WK664glGjRvHqq6+SkpLCokWLfPpt3bqVPn36sGjRIgYOHMgHH3zAW2+9RdeuXfn000955ZVXCu177NixTJkyhRdeeIFDhw4xZMgQ9u7dW1ZvrdLq2bMnmzZtYsuWLYW2uVwuvvrqK6655hoMw2D+/PlF7mPu3LnUqFGD5s2be9vuuOMOpkyZwscff8xjjz2G3W7n3nvvLfSFgBRPeHg4U6ZMYcqUKTz55JOsX7+eYcOGceLEiRI/lj67ojmdx9i1+1N++LEvq76/jl27xlMlqh0tmn9Mxw7fULfOo4WSwPkS4m+gY8dvSem+hY4dv1USWERERERESozfZwQHBAQwYsQI7r33Xm699VZ69eoFwOzZs3G73fzzn//09t27dy/XXXcdbdu2Zfz48d72Rx55hJ9++om33nqL7777jmbNmnHkyBEWLlzIsWPHePDBB70zBi9WKhEh5c2ECRPo378/jz76qLete/fu3H///T79Hn/8capUqcKkSZO8NcUBOnXqxB133MGSJUsK7btp06aEhoYC0KRJE7p168asWbO48847S+fNXCRSUlIIDg5m9uzZPPzwwz7bVq1axYEDB+jVqxc7d+5kzpw5DBw40KfP8ePHWbp0KcOGDfNpT0pK8vmb1Lt3bzp27MgzzzxD27ZtufHGG0vpHVVOVqvV+/Ns0aIFCQkJDBw4kKVLlxa68uZC6bM7yTRNjhz5nj1pU9m3bz5udzahofWpV+9Z4qvdQEDAxT0OERERERER//P7jGCAbt268cknn5CcnMzMmTP5/PPPadasGZMmTaJZs2ZnfX2zZs2YOnUqPXv25M8//2TcuHF89dVXJCcn89///pf77ruvDN5F+de2bVtq1qzJvHnzOHr0qL/DkXJizrY5XD39apqNbcbV069mzrY5ZXLcY8eOUbVq1ULtBRc0/P777/n99995/PHHfZLA+cLCwrxfHp1OQkIC0dHR7N69+8KDLkcy1+wjbeT37HrqW9JGfk/mmn2lfsyQkBC6devGvHnzCm2bM2cOMTExXH755fTs2ZPVq1eTnp7u0yc1NZXs7Gx69ux51mPddNNNNG/enEmTJpVY/GXps/RDtP7uNxIWr6X1d7/xWfohv8XSpEkTAHbv3k2TJk0K/f3fvHkzycnJfPfdd4Anofn222/Tvn17WrZsyRNPPFHkgq6nU9E/u3OVnZPO9u3vsmJld1avuY39+78mIaEvbVrPpF3budSscbuSwCIiIiIiUi74fUZwvssvv5zLL7/8jH2qV6/Oxo0bi9zWoEED/vvf/5ZGaJVGfomI9957j1mzZnHbbbf5JN3k4jNn2xxGfDeCbFc2AGmZaYz4bgQAPeucPVl3IRo1asSECRNITEyka9euVKlSpVCfH374AZvNdta/DWdy/PhxMjIyikw6V1SZa/ZxZMZmTIcbANeRHI7M2AxAaMu4Uj12z549mTt3Lr/++qs3wehwOFi4cCG9e/fGarXSo0cPXnzxRebOncsdd9zhfe3cuXOpX78+9evXL9axOnTowAcffFBk/ffy7LP0Qzy+cSdZbk8Znl05Dh7fuBOAfvFlnxDM/xIk/9/AwoUL6devn3f73LlzqVq1Ku3atQNg3LhxvPPOO9x99920bt2ahQsX8p///OecjllRP7vicrtzOXBgMXvSpnLw4DeAm6iodlxS+0Hi4q7Bag32d4giIiIiIiKFlJtEsJQNlYiovL7c+iUzN888p9es27+OXHeuT1u2K5vnlz/P9E3Ti72fG+vdyPV1rz+nYz///PPcd999PPXUUxiGQd26dbn66qv5y1/+4p39u2/fPqpUqUJgYKDPa10ul7fWtWEYWK1Wn+1utxun08n+/ft59dVXMU2z3C5elfnTXjJ/PLf6xbk7joLLt9a36XBzePomMr9PP82rCgttXY3Qy6qd07E7d+5MREQEc+bM8SaCly1bRkZGhnemb3R0NB06dGDOnDneRHBGRgbLly8vVPrjTOLj43E6nX5N5E9NP8SktIPn9JqfMk6Qe0ot9iy3ySMbdjLhHPY1ICGGW84zcex0OgHYuXMnI0aMIDQ0lA4dOtCpUyfmzp1bKBHco0cPrFYrLpeLDz74gP79+/PII48AnjIst99++znV2S4Pn11pOH58E2lp00lLn3ly4bda95CQ0I+QkNr+Dk9EREREROSMlAi+CLVt25bff/+defPmUadOHSIiIvwdkvjJqUngs7WXpAYNGjBv3jyWLVvGsmXLWLlyJe+++y5z585lxowZ3hq/RWndurV34auoqChWrVpVaHu+KlWq8PLLL9OwYcPSeSP+4DrNgo+nay9BAQEBXHXVVcyfP58nnngCwzCYO3cuSUlJPoty9uzZkyeffJKdO3dSo0YNFixYgMPhKFZZiHwVdWHLU5PAZ2svaUeOHKFx48be54mJibzxxhvExcVx3XXX8dRTT3H48GGqVKnC+vXr2b59Oy+99BIAaWlp7N+/n5SUFJ99XnXVVd7SEcVRUT+7ojidx9i7dzZ70qZz9OhaDMNO1aopJCbeTEx0JwzDevadiIiIiIiIlANKBF+EVCKicrq+7vXnPCv36ulXk5aZVqg9ITSBT675pKRCO62AgAC6d+/una07bdo0nn32WaZPn87QoUOJi4vj8OHD5ObmEhAQ4H3dxIkTcblcTJ06lQULFhTa78SJEwkKCqJKlSokJCRgsZSLcuhFCr3s3Gflpo38HteRnELt1qhA4u4+e131C9WrVy8+++wz1qxZQ+PGjUlNTS30d+TKK68kMDCQOXPmcM899zB37lyaN29OjRo1in2cvXv3YrfbiYyMLI23USy3xEef86zc1t/9xq4cR6H26oF2ZrasV1KhnVZ4eDiffPIJhmEQGxtLXFyc97Pp3r07NpuNBQsW0L9/f+bOnUt8fDyXXXYZAAcOHAA8V48UdOrzsykPn92F8Cz89kPewm/z8hZ+q0e9S58hPv4GAgLO7echIiIiIiJSHpTf7IiUqvwSEZs3b+bnn3/2dzjiJw+1eogga5BPW5A1iIdaPeSXeG6++WaioqLYtm0bAG3atMHpdLJy5Uqffo0aNaJp06bExRVdD7dhw4Y0adKEpKSkcp0EPl8RPWpj2H3fl2G3ENGjdpkcv127dlStWpW5c+eyZMkSMjMzCy3aFxYWRteuXZkzZw4HDx5k1apV5zQbGGD58uU0bty4wtWYfbpOAsEW3y/Xgi0GT9dJKJPjW61WmjZtSpMmTahWrZpPgj40NJQuXbowd+5cAObNm8c111zj7ZNfxuHgQd8SFqc+P5uK+tl5Fn57jxUrU1i9ZgD79y8kIf5GWreeQbu286hZ8w4lgUVEREREpMKqfBkSKba2bdtSs2ZN5s2bV2gVebk49KzTkxEdRpAQmoCBQUJoAiM6jCj1heKg6MTSoUOHOHbsmDcZ1aZNGxo1asR///tfjh8/XuoxVRShLeOI6lsPa5SndrI1KpCovvVKfaG4fFarlWuvvZb58+cze/Zs6tatS4MGDQr169WrF5s2beKdd97BNE2uvfbaYh9j+vTprFu3jgEDBpRk6GWiX3w0ryXXoHqgHQPPTODXkmv4ZaG4ovTs2ZMffviBRYsWsXPnTp8EfUJCArGxsaSmpvq8ZuHChcXef0X77NzuXPbt+4q1P9/J8uWd2LrtNQID42nU8DU6XbGSBg1eJDKiua6cERERERGRCk+lIS5iKhEh4EkGl0Xi91S9e/cmJSWFjh07EhMTw+7du/n4448JCgqiT58+gGchuNdee42hQ4fSt29fBg0aRP369XG73Wzfvp25c+eesZZwZRbaMq7MEr9F6dmzJ+PHj2fhwoU88MADRfbp0qULYWFhfPrpp7Rr1+60M7h3797N2rVrcTqdpKenk5qayrx58+jXr5/3d6Gi6RcfXW4Sv6fq0qULQUFBPP/881SvXp1mzU6WE7Fardx55528+uqrVKlShdatW7NgwQK2bt1a5L4q8md3PHMzaXumnVz4LaAatWvdTULCTVr4TUREREREKiUlgi9y+SUi5s+fz88//0yLFi38HZJcJO677z5SU1N58cUXycjIIDY2lpYtW/LGG2/41JGtW7cuM2fO5MMPP2TChAmkpaVhs9moXbs21157LYMGDfLju7h4tWzZkqSkJHbv3l2oLES+wMBArrrqKmbOnHnaPgAff/wxH3/8MQEBAURHR9O0aVPeffddb+1oKVlBQUF0796dWbNmcddddxXaPnToUI4cOcLkyZMZO3Ys3bt3Z/jw4Tz++OOF+la0z86z8NucvIXf1mAYNs/Cbwk3Ex3dCYtFwyIREREREam8DLMyLe1dAnbt2kVKSgqpqalUr17d3+GUCbfbzZgxY9i7dy/33XcfERER/g5JzmL9+vU0bNjQ32GIiFyQsvhbZpomRzJ+JG3PVPbum4fbnUVoaD0SE24mPr6Pav7KaVXEMWFFjFlERERESs7ZxoOa+iLeEhGjRo3irbfewul0EhkZSUpKis8lwyIiIhVFTs5e0tJmsidtGllZ27Faw4iPv4HEhJuJUM1fEREREang5mybw5ur3yQ9M5340HgeavWQX8o+SsWiRLAAnjqPhmHgdDoByMjIYNasWQBKBouISIXgdjs4eHAxe/ZM4+ChpZimi6iotlxS+z7i4q7Bag3xd4giIiIiUsGVhwTsnG1zGPHdCLJd2QCkZaYx4rsRAEoGyxkpESwApKam4na7fdocDgepqalKBIuISLmWmbmFPWnTSEubicNxkICAOGrWvIvEhH6EhFzi7/BEREREpJK40ASs23ST7cwmy5lFljPL+zjb5bk/4Txxsq1Av1P7r0pbRa4712ff2a5s3lz9phLBckZKBAvgmQF8Lu0iIiL+5HQeZ+++OaTtmUaGd+G37iQm3KKF30RERESkVLy5+k1vEjhftiubF1a8wPLdy8l2Zfskc32Svc7sQq8tjiBrEEG2IIJtwd77U5PA+dIz08/rfcnFQ2dJAkBkZGSRSd+wsDA/RCMiIlKYaZpkZPzEnj1T2btvLm53FiEhl3LppU+TEN+HgICq/g5RRERERCqx0yVaTzhP8NPenwi2BXsTtjFBMd7EbcFbwbYgWxAhthCCrEGn3W4xLIWOd/X0q0nLTCvUHh8aX+LvWSoXJYIFgJSUFGbNmoXD4fBpz8nJYd++fcTFxfkpMhERudjl5OwjLX0maWnTOHHij7yF367PW/ithRZ+ExEREZEyER8aX2QCNiE0ga9u+qrM4nio1UM+JSry9a7bu8xikIqp8NcKclFq1qwZvXv3JjIyEvDMEL7qqqsIDAxk3LhxHDhwwM8RiojIxcTtdrB//wJ+XncXy7+7gq1b/02AvSqNGv6bTlesoGGDl4mMbKkksIiIiIiUmYdaPUSQNcinLcgaxEOtHirTOHrW6cmIDiNICE3AwCA+JJ7owGhmb53NsdxjZRqLVCyaESxezZo1K7QwXL169RgzZgxjx47l9ttvJzo62k/RiYjIxSAzcyt70qaesvDbX0lMuEkLv4mIiIiIX+UvxPbm6jdJz0wnPjSeh1o95JcF2nrW6elz3J/3/8zQeUMZ+f1IXrripTKPRyoGJYLljOLi4hgyZAhjx471JoOjoqL8HZaIiFQiTudx9u2by560aWRkrPYs/BbTjcTEW4iO7qyF30RERESk3Dg1AVteNI9tzl3N7uK9n9+jc/XO9Kjdw98hSTmk0hByVvHx8QwePJjs7GzGjh3L0aNH/R2SiIhUcKZp4nbn8Pv6J1m2vD3rNzyNw5HBpZc+RceOy2nW7H2qVu2uJLCIiIiISDH9tdlfaVq1KS+seIG9mXv9HY6UQ0oES7EkJiYyePBgMjMzGTt2LMeOqeaMlJyvvvqKIUOG0Lp1a5o0aUKPHj145ZVX2LvX8x9XcnKy99asWTO6du3K/fffz6JFi067z507d5KcnEzXrl0xTbOs3oqInIXb7SAnZz+ZmZvJzT3Avn3zqBbXi9aXTePydl9Rq+ZfCQyo6u8wRUREREQqHLvFziudXsHhdvDs8mdxm25/hyTlzDlPs9m0aROzZs1iy5YtZGVlMWbMGADS0tL4/fffadu2LeHh4SUdp5QD1atXZ+DAgUyYMIFx48YxbNgwQkND/R2WVHAjR45k7Nix9O3bl2HDhhEWFsaWLVuYPHkyu3bt4p133gHgjjvuoEePHjidTtLS0li0aBH33nsvN954I6+88kqh/c6dOxfw/G366aefaN26dZm+LxE5yTTdOJ3HcTgO43R6riqxWkOw26twRccV2Gz6v0REY2wREREpCbUiajG8zXBeWPECn67/lEGNBvk7JClHzikR/P777/PWW2/hdnu+USi4UndOTg73338/zz33HLfddlvJRinlRq1atbjtttuYOHEi48aNY+jQoYSEhPg7LKmgFi1axCeffMJLL73ETTfd5G1v27Yt/fv3Z9myZd62pKQkWrRo4X3eu3dvOnbsyDPPPEPbtm258cYbffY9e/ZsWrRowaZNm5g9e7YSwSJ+4HJl43AcxuE4gmk6MQwbAQGx2O1VsFoDsVrXKwksgsbYIiIiUrJuqncTS3cu5Y2f3qBdQjvqVann75CknCh2aYivv/6a//u//6N+/fq88847DBgwwGd77dq1adCgAYsXLy7xIKV8ueSSS7j11ls5cOAA48ePJysry98hyQXImDWLzd1TWN+wEZu7p5Axa1aZHXvMmDE0btzYJwmcz2q10qVLlzO+/qabbqJ58+ZMmjTJp33z5s1s2rSJvn370r17d+bPn4/T6SzR2EWkaKbpIjf3EJmZW73lH6zWEIKDaxEW1oCgoHis1kB/hylSbmiMLSIiIiXNMAz+2eGfhAWE8fS3T5PryvV3SFJOFDsRPHbsWGJjY5kwYQIpKSnExMQU6nPppZfyxx9/lGiAUj5deuml9O/fn7179zJx4kRycnL8HZKch4xZs0h77nmce/aAaeLcs4e0554vk2Sww+FgzZo1dOrU6YL206FDB3777TccDoe3bfbs2djtdq6++mp69uzJ4cOH+e677y40ZBE5DdM0cTozycraxfHjG8jO3o1puggMjCcsrAEhIbWw2yN8ZjmKiIfG2CIiIlIaYoJjeKHDC2w8vJFRa0f5OxwpJ4pdGmL9+vVcd911hIWFnbZPtWrVOHDgQIkEJuVf/fr1ufnmm5k6dSoTJ05k0KBBBAQE+Dusi9aRzz8n47MZ5/SarJ9/xsz1/WbQzM4m7ZlnOTJ1WrH3E9mvL1F9+pzTsY8cOUJubi4JCQnn9LpTxcfH43Q6ycjIoGpVzwJTc+fOpX379lSpUoUrrriCyMhI5syZQ+fOnS/oWCLiy+124HAcweE4jNudg2FYsNki80o/hCjxK1IMGmOLiIhIaelSows317+ZMb+OoVNSJ9rEt/F3SOJnxZ4R7HA4zrow2NGjR7FYir1LqQQaNmxIv3792LlzJ5MmTfKZlSnl36lJ4LO1l4YLTRSZpunzfN26dezYsYOePXsCEBAQwFVXXcXXX3+tmesiJcA0TRyOo5w48SfHj28gJycdw7ASFJREWFgDgoOrY7OFKgksUkwaY4uIiEhperz149SMqMkzy57haO5Rf4cjflbsGcEJCQls2LDhjH1+++03ateufaExSQXTpEkTXC4XM2fOZPLkydx6663Y7XZ/h3XRierT55xn5W7unuIpC3EKW2IitcaPK6HIihYVFUVAQAB7ijj+udi7dy92u53IyEjgZFmItm3bcvSo5z+5rl27Mn36dJYsWUKPHj0uOHaRi5HLlYPDceiUhd+qYrdHq+avyAXQGFtERERKU4g9hFeueIXB8wbz8qqXGdlppL9DEj8q9tSCbt26sXLlSpYvX17k9nnz5vHbb7/RvXv3EgtOKo7mzZtz/fXXs3XrVqZNm6aFuSqIuEcexggK8mkzgoKIe+ThUj+23W6nVatWLFu27IL2s3z5cho3bozdbsftdjNv3jwcDgfdunWjTZs2tGnThvvvvx+AOXPmlEToIhcN34XfNp2y8FsyQUEJSgKLXCCNsUVERKS0NY1tyt3N72bOtjnM+2Oev8MRPyr2jOC//vWvzJo1i7vvvpubb76Zffv2AZ5anN9//z2fffYZ8fHxDBkypNSClfKtVatWuFwu5syZw2effcZNN92E1Wr1d1hyBpG9ewOw743/w5mWhi0hgbhHHva2l7ahQ4fyt7/9jZkzZ3LjjTf6bHO73SxbtuyMdX2nT5/OunXrePXVVwH4/vvv2bdvH48//jjNmjXz6TtjxgzmzZvH8ePHz1iHUeRiZ5omLtcJHI7DOJ0ZmKYbiyWQwMB47PYoLBZd8SFSkjTGFhERkbLw16Z/ZdnuZfxr5b9oGdeS+NB4f4ckflDsRHB0dDSffPIJDz74IJMmTfK2P/bYY5imSe3atXnnnXeIiIgolUClYmjTpg0ul4v58+czc+ZM+vbtq5p25Vxk795llvg9Vffu3bn99tt55plnWL16NSkpKYSEhLBt2zYmT55MUlKSNxG8e/du1q5di9PpJD09ndTUVObNm0e/fv3ok1cSY86cOURERDB06NBCCxeGhYXx+eef8/XXX3v7i8hJWvhNxD80xhYREZGyYLPYGHnFSPrN6sezy55l9NWjsRjK11xsip0IBqhXrx5z5sxh6dKlrF69miNHjhAeHk7Lli3p1q0bNts57U4qqcsvvxyn08nXX3+N1WrlhhtuUDJYTuupp56iZcuWTJgwgccee4ycnBySkpLo3r07d9xxh7ffxx9/zMcff0xAQADR0dE0bdqUd99913uprMPhYMGCBVxzzTWFksAAjRs3pm7dusyePVuJYJE8pmnidB7Lm/17DDCxWkMICkrCbo/EMHRVh0hZ0BhbREREykKNiBo82eZJRqwYwYTfJzCksa44utgUe1T59NNPk5yczLBhw+jWrRvdunUrzbikgrviiitwuVwsXrwYq9VKr169lAyW0+rRo8cZF3HbuHHjWfdht9tZtWrVGfvMnTv3nGMTqYw8C78dxuE4XGDht5i82b9BZ9+BiJQYjbFFRESkLPWt15elu5byf6v/j8sTL6d+lfr+DknKULEzc7Nnz+bgwYOlGYtUMl26dKFTp06sXr2aefPmYZqmv0MSEbloFV74bT9Wa/ApC78pCSxS1jTGFhERkbJkGAYjOowgIiCCp759ihxXjr9DkjJU7ERwUlKSBqlyzrp3706HDh344Ycf+Oqrr5QMFhEpQ57SD5lkZe3i+PENZGfvxjSdBAbGExbWgJCQ2tjtERiqDSbiNxpji4iISFmLDormhY4vsPnwZt5e/ba/w5EyVOwzv169evHNN9+QkZFRmvFIJWMYBldddRXt2rVj5cqVfP3110oGi4iUMrfbQU7OfjIzN3PixDYczgxstkhCQuoQGlqfwMBYLBa7v8MUETTGFhEREf/oXL0z/ZP7M+73caxKO3OZRak8ip0Ivvvuu2nSpAlDhgxh8eLFHDhwoDTjkkrEMAyuueYaWrduzfLly1myZIm/QxIRqXRM08ThOMqJE39y/PhGcnLSMQwrQUFJhIc1IDi4OjZbKIZh+DtUESlAY2wRERHxl8daP0atiFo8s+wZMnL0pfTFoNiLxTVr1sz7+N577z1tP8Mw+P333y8sKql0DMPguuuuw+VysXTpUqxWK507d/Z3WBWaaZpK6IhIhV34TVeHiHhojC0iIiL+EmwLZmSnkQyaO4iXVr3Evzv/298hSSkrdiI4ISGhNOOQi4DFYqF37964XC4WLVqEzWajQ4cO/g6rQrLb7WRlZRESEuLvUETED0zThcNxFIfjEC7XCQBstnDs9irYbOEVouZvVlYWdrvKU4hojC0iIiL+1LhqY+5pfg+j1o6iS/Uu9KzT098hSSkqdiJ40aJFpRmHXCQsFgs33HADTqeTBQsWYLVaadeunb/DqnDi4uLYvXs3SUlJBAcHa2awyEXANE1criwcjkM4nRmYphuLJYDAwHjs9qgKU/PXNE2ysrLYvXs31apV83c4In6nMbaIiIj421+a/oVlu5fx0sqXaBXXioQwfVFdWRU7ESxSUqxWK/369cPtdjNv3jysViutW7f2d1gVSkREBAB79uzB4XD4ORoRKU2m6cLlysLlysQ0nYCB1RqM1RqCxeIGDuTdKg673U61atW8f8tERERERMR/bBYbL3d6mZu+vIlnlj/Dh1d/iKUCXGUo5+68E8FZWVkcO3aM8PBwgoODSzImuQhYrVZuuukmpkyZwuzZs7FarbRs2dLfYVUoERERSqKIVFJut5NDh75lT9o0DhxIxTSdRES0JDHxZqrFXYfNFu7vEEWklGiMLSIiIv5QI7wGT7V9iue/e55xv41jWJNh/g5JSsE5JYJN02Ts2LFMnTqVP/74w9t+ySWXcMsttzBkyBAsFn1jIMVjs9m45ZZbmDRpEl988QVWq9VnwRQROTdp6V+wbetrZOekERSYQJ26j5MQf4O/w5JzcOLEdvakTSc9bQY5uXux26OpUX0oCYk3ExZaz9/hiUgp0RhbREREyoM+l/Zh6a6lvLXmLdontic5OtnfIUkJK3Yi2OVyce+99/LNN99gmiaRkZHExcWxb98+tm3bxquvvsry5ct5//33sVqt5xzIqlWrGDVqFL/++itWq5UWLVrw6KOP0qhRo2Lv4/Dhw4wePZrU1FTS0tIIDQ3l0ksvZeDAgVx77bXnHJOUPrvdzq233sqnn37KzJkzsVqtNG7c2N9hiVQ4aelfsGHDM7jdWQBk5+xhw4ZnAJQMLudcrhPs2zefPWnTOHLke8BCTEwX6if+g6ox3bBYAvwdooiUotIeY4uIiIgUl2EY/KP9P+j7ZV+e+vYpJveaTKA10N9hSQkq9tSCiRMnsnTpUlq1asVnn33GqlWrmDVrFqtWrWLGjBlcdtllLFu2jIkTJ55zEEuWLGHYsGFs3LiRG2+8keuvv55169YxYMAA1q1bV6x9bN++nRtuuIHx48eTnJzM7bffzjXXXIPL5WLFihXnHJOUnYCAAAYMGED16tX57LPP2LBhg79DEin3TNMkJ2c/R478SFraDDZu/Ic3CZzP7c5iy5aXcbtz/BSlnI5pmmRkrGX9hmf4dll7fl8/nJycvdSt8zhXdFxGi+YfEhfbQ0lgkYtAaY6xRURERM5VlaAq/Kvjv9hyZAtvrn7T3+FICTNM0zSL07Fv374cO3aMOXPmEBBQ+MQ0NzeXnj17Eh4ezowZM4odQG5uLldddRUZGRnMmDGDOnXqALB161b69etHnTp1zro/h8NB3759OXToEGPGjKFePd/LZ51OJzZb8SY/79q1i5SUFFJTU6levXqx34dcuOzsbMaPH09aWhoDBgwo9DmKXGzc7lyys3eTlbWDE1k7yPK57SyU+D0dw7ATHt6IiIgWREa0IDKyJUFB1TEMo5TfgZwqN/cg6emfsydtGpmZm7FYgqkWdy0JCTcTFdVGn4lIOVJWY8KSHGNrHCsiIiIl5aWVLzF542Q+uPoDLk+43N/hSDGdbTxY7NIQf/zxB/379y9ygAqeWZ0pKSlMmTLlnAL87rvvSE9Pp3///t4kMEDdunXp3bs3U6dO5ffffz9jiYg5c+awadMmRo4cWWTysLhJYPGvoKAgBg0axNixY5k8eTK33XYbdevW9XdYIqXK4ThKVtafZGXvJOvEjpOPs3aQnZ0GuL19LZYggoNrEBxci+jojgQH1yQ4uCYhwbVYvWYwOTlphfZvt0eTmHAzGUfXsmfPVHbtGpvXHkNkZEsiI1oQEdmCiPCm2GxhZfW2Lyqm6eLgwW/YkzY9b+E3BxERLWiQ/BLVqvXUwm8iF7nSGmOLiIiIXIhHWz/KqvRVPLPsGWZcP4PIwEh/hyQlwO8Z0h9//BGA9u3bF9rWvn17pk6dyo8//njGRPBXX32FYRhcddVVbN26lWXLluFwOKhfvz4dO3ZUPbUKJDg4mCFDhjBmzBgmTZrEoEGDqF27tr/DEjlvpukmJyfdZzbviQKzep3OIz797fYYgoNrEhXZmuD4mt7Eb3BwTQICYk87Y7Ru3eE+NYIBLJZg6tV71lsj2O12kpm5iYyjazmasYaMo2s5cODr/N6EhdYjIrIFkREtiYhsQWhIXQxDixOdrxMn/iQtbRpp6TPJyUnPW/htCAkJNxEWVt/f4YmIiIiIiJxWsC2YkZ1GMnDOQP618l/8p/N/dAVjJVDsRPAll1xCamoqjz766GkvW1u0aBGXXHLJOQWwY8cOAGrWrFloW35bfp/T+e2334iOjmbs2LG8/fbbFKx2kZyczPvvv09iYuI5xSX+ExIS4k0GT5w4kcGDBxf5+yFSXrhc2d5ZvJ7bn2Rl5T/fhWnmevsaho2goESCg2oSUa2pZ1ZvUM282b01zntWbn6yd9vW18jOSSMoMIE6dR/3WSjOYrERHt6I8PBGkHQbAA5HBkeP/uxNDu/bN489ezyzzqzWMO+MYU9JiRbY7VXO98d0UXC5sgos/LYK78Jv9Z6nalUt/CYihZXWGFtERETkQjWKacR9Le/jzdVv0qV6F3rX7e3vkOQCFTsRfP311zNy5Ej+8pe/8PTTT/vM0N2wYQMjR45k586dPPnkk+cUwPHjxwEICyuc/MhvO3bs2Bn3cfjwYVwuF++99x5///vf6dWrF9nZ2XzwwQd8+umnPPjgg0ybNk3fXFQgYWFhDB06lE8++YSJEycyZMgQkpKS/B2WXKRM08ThOOSdxetJ9O7wJntzcvf69LdawwgOrkloaH2qVk3xlm8IDq5BYGAiFkvpXIyREH+DT+K3OOz2SGJiOhMT0xnwzGA+cWI7R496ZgxnZKxl+/Z3yS9RERxcyztjODKiBWFhDbBY7CX9VioU0zQ5emwdaXumkb53Fi7XcYKDa1G3zuPEJ9xIUGC8v0MUkXKstMbYIiIiIiXh9sa38+2ub3l51ctcVu0yEsM00bIiK3Y2YtCgQXz77bcsX76cfv36ER4eTtWqVTlw4ADHjh3DNE3at2/PoEGDSjPeIpmmicvlYvDgwQwZMsTb/o9//IP169ezZs0afvrpJ1q3bl3mscn5Cw8P9yaDx48fz9ChQ0lISPB3WFJJud1OcnL2FLEomyfh63Id9+kfGBhPcFANoqOv8Nbqzb/Z7VUq7BdPhmEhNLQOoaF1SEjoB4DLdYKjR3/1JocPHV5O+t7PAbBYAgkPb0pkfkmJiOYEBV0c/05zcw+RvvcL9uyZSmbmJiyWIOLiriUx4WaiotpW2N8BESlb5XmMLSIiImK1WHnpipe4adZN/H3Z3/no6o+wWlSCtaIqdiLYZrMxevRoPvnkE6ZNm8aff/7J0aNHAahVqxY333wzw4YNO+eF2fJn/ebPDC4ovy08/MwL6YSFhXH48GG6detWaFvXrl1Zs2YNv/76qxLBFVBkZCRDhw5lzJgxjBs3jmHDhlGtWjV/hyUVlNN5vEDJBt9Zvdk5uzFNl7evxRJAUFANgoNrEBXVxjfZG1QDqzXIj++kbFmtIVSp0pYqVdoCni/fcnLSyMirM3w0Yw27do1jh/tDwJMkj8grJREZ0ZLw8CaV5udlmi4OHvqWtD3T2X/g67yF35rTIPlFqlXrpYXfROScldYYW0RERKSkVA+vztNtn+bZ5c8y9vex3NHkDn+HJOfpnEaUVquVO++8kzvvvJPMzEyOHz9OWFgYoaGh5x1AwTrAjRs39tl2pvrBBdWuXZvDhw8XmTDOb8vJyTnvGMW/qlSp4p0ZPHbsWG6//XZiY2P9HZaUQ6Zpkpu7jxNZO8g+ZVG2rKw/cTgO+fS32aIICa5JREQzqgX39C7K5inhEK+F0k7DMAyCghIJCkqkWrWeALjduRw/vqFAcngt+/fPz+tvIyysQYGSEs0JDq5doWbMehZ+m05a+gzvwm/Vqw8mMeEmwsKS/R2eiFRwpTHGFhERESlJ19e9nqW7lvL2mrfpkNiBBtEN/B2SnIfznloQGhpaIoPT1q1b88EHH7BixQquvfZan20rVqzw9jmTdu3asWbNGrZu3Voombx161YAlRSo4KKjowslg2NiYvwdlviB251DVtbukzN6fRZp24nbnV2gt4WgoASCg2sSG3uVZ1G2kJreBdrs9gi/vY/KxmIJICKiGRERzajBUABycw+QcfRnjuYlh9PSZ7Br93gA7PYqREQ0z1uMriWREc3L3WxalyuLffu/Ys+eqQUWfutM/XrPUbVqdy38JiKloqTG2CIiIiIlyTAMnr/8edbuW8tT3zzF5F6TCbJVjis/LybFTgRv2bKF7777jp49exaZgDt48CBz5syhY8eO1K1bt9gBdOjQgfj4eL788kuGDRtGnTp1AE8Cd9asWTRq1Mi7aIbD4WDHjh0EBweTmHiyOHXfvn356KOP+OCDD+jevbu33MT27duZOXMmISEhdOrUqdgxSflUtWpVb5mI/GRwlSpV/B2WlAKH44hPjd6CdXtzctIB09vXYgkmJLgmwcG1iInu7J3RGxxck6CgJCXr/CggoCqxVVOIrZoCeMoqZGZu8c4Yzji6hoMHl+L5PA1CQy/1lJSIaEFEZAvCQuthGGVbe8o0TY4d+4U9adNIT//Ss/BbUE3q1HmUhPi+F039YxEpO6U1xhYREREpaVFBUbzY8UXu/vpu3lz9Jk+21WK2FU2xE8EfffQRS5cuZeDAgUVuj4yM5H//+x+bNm3ixRdfLHYAAQEBjBgxgnvvvZdbb72VXr16ATB79mzcbjf//Oc/vX337t3LddddR9u2bRk/fry3vVatWjz00EO89tprXH/99XTr1o2srCwWLFhAVlYW//rXv5QwrCTi4uIYMmQIY8aMYcyYMdx+++1ERUX5Oyw5R6bpIjs73TOr12dGr+fmdB716R8QUJXg4JpUiWp3sk5viGdWb0BA1QpVYuBiZhhWwsKSCQtLJimxPwBO5zGOHl3nLSlx4EAqaWnTALBaQ4kIb5o3Y9iTHA4MqFoqseUv/Ja2ZxrHMzfmLfx2TYGF31QmRERKR2mNsUVERERKQ4ekDgxsOJAJ6yfQqXonOiR28HdIcg6KnQj+8ccfad++PVZr0bOzbDYbHTp04IcffjjnILp168Ynn3zCO++8w8yZMzEMg1atWvHII48UKvVwOn/9619JSkrik08+4bPPPsMwDJo0acJf//pXOnfufM4xSfkVHx/PkCFDGDt2rHdmcESELvEvb1yuEwUWZjtZp/dE1g6ys3djmg5vX8OwERSURHBwTSIiWhAcXMM7yzcoqDo2my6RraxstnCiozsSHd0R8MzIzcrawdGjnhnDGRlr2bHjA0zTCUBQUI28Reg8JSXCwxpgsQSe17FN08WhQ8vYs2fayYXfwpuRnPwv4qv1LnelKkSkcirNMbaIiIhIaXi41cOs3LOS55Y9x2fXf0ZUUJS/Q5JiKnYieN++fT7lGIoSHx/P119/fV6BXH755Vx++eVn7FO9enU2btx42u3XXXcd11133XkdXyqWxMREBg0axPjx4xk7dizDhg0rcrFAKT2maZLrOHjKomx/epO/ubn7ffpbrWGEBNciPKwhcbE9CpRwqEVgYDwWi1ZDF0/dqZCQWoSE1CI+/gYAXK5sjh371VtS4siRH9m7d1Ze/wDCwxufTA5HtCQoKNE7Szwt/Qu2bX2N7Jw0ggITqFP3caIiW7InbTppaZ/lLfxWherVB5GYcLMWfhORMlfaY2wRERGRkhZkC+KVTq9w29zbeGHlC7ze5XVdqVtBFDvzYrfbOXHixBn7nDhxQh+8lJkaNWowcOBAJkyYwLhx4xg2bJgWVylhbreD7OzdPjN6Ty7QthOXK9Onf2BgvKdWb0yXkyUcgmsSElwTmy1Kfx/kvFitQURFtSYq6uTCodk56RzN+JmMo2s4mrGW3bsnsXPnJwAEBMQSGdECwwhg/4GFmGZu3mv28PvvjwNuPAu/daJevWeJrdr9vGcVi4hcKI2xRUREpCJqGNOQ+1vcz/+t/j9mbZvF9XWv93dIUgzFTgTXrVuXb775BrfbjcVSuFaiy+Vi6dKlXHLJJSUaoMiZ1KpViwEDBvDpp58ybtw4hg4dSkhIiL/DqlCczmOFFmTLv2Vn78GTNPOwWAIICvIkdqtEXe6d0etZmK06VquSaVI2ggLjCYqLJy6uB+D50uJ45kbvInRHj/7MiRN/FPFKNzZbOO3aztPCbyJSLmiMLSIiIhXVsMbD+Hb3t7y86mUuq3YZSWFJ/g5JzqLYieBrr72WkSNH8tRTT/Hcc8/5XIZ/7NgxXnjhBXbv3n3ahS5ESkudOnW49dZbmTRpEhMmTGDIkCEEBQX5O6xywzTd5OTsLVCv13eBNofjsE9/uz2a4OAaREa2JD7+BoKDTi7OFhgQp0WzpFyyWOxEhDchIrwJ1RkEQOqiSwGzUF+n87iSwCJSbmiMLSIiIhWV1WLl5Stept+X/fj7t3/n4x4fY7UUve6BlA/FTgTfdtttzJ49my+//JJvvvmGFi1aUK1aNfbu3cvatWs5cuQITZs21SBV/OLSSy/llltuYcqUKUyYMIHBgwcTGFi5ZqcWVes0wVtDNYfsAsndEwUWaMvO3onbnePdj2FYCQxMJCS4JrGxPbyLsnlm99bUAllSaQQFJpCds6fIdhGR8kJjbBEREanIEsMS+Xu7v/P3ZX/nk98+4c6md/o7JDmDYieCAwICGDNmDP/617+YNWsWS5Ys8W6zWCzccMMNPPfccwQEBJRGnCJnlZyczM0338zUqVOZOHEigwYNqjS/j2npX7BhwzO43VlAfq3T4fzxxyjc7hPk5KT79LdaQwgOrkloaB2qxnTxlm/wlHBIxGKx++NtiJSpOnUf9/l3A2CxBFOn7uN+jEpExJfG2CIiIlLR9arTi6W7lvLOmnfokNiBRjGN/B2SnEaxE8EAYWFhvPrqqzz11FOsW7eOo0ePEhERQbNmzahSpUppxShSbA0bNqRfv3589tlnTJo0idtuuw27veInPbdtfc0nmeXhIjt7F9Wq9fTO6A3JS/ba7TFaVEYuevkz5k83k15EpLzQGFtEREQqMsMweO7y51izbw1PffsUU3pNIdgW7O+wpAjnlAjOV6VKFbp06VLSsYiUiCZNmuByuZg5cyZTpkzh1ltvxWY7r1/1ciM7J63IdtN00LjRa2UcjUjFkRB/gxK/IlJhaIwtIiIiFVVkYCQvdnyRuxbexRs/vcHf2/3d3yFJES4oO3bo0CFWr16NaZq0bNmSqlWrllRcIhekefPmuFwuvvzyS6ZOncott9xSoZPBqnUqxbFu3TpSU1PJyMggMjKSlJQUmjVr5u+wRETkHGmMLSIiIhVR+8T2DGo4iAnrJ9C5emeuSLrC3yHJKSxn2rhlyxY++OADNm7cWGjb559/Tvfu3XnggQd48MEH6datGxMnTiy1QEXOVatWrbjuuuvYtGkTn332GS6Xy98hnbc6dR/HYvG9rEK1TqWgdevWMWvWLDIyMgDIyMhg1qxZrFu3zs+RiYjIqTTGFhERkcrq4cse5tKoS3lu+XMczj7s73DkFGdMBH/xxRe88cYbREZG+rRv3bqVZ599luzsbBISErjkkktwOp289NJLSjpIudK2bVt69OjB+vXrmTlzJm63298hnZeE+Bto0OAlggITAYOgwEQaNHhJl7yLV2pqKg6Hw6fN4XCQmprqp4hEROR0NMYWERGRyirQGsjITiPJyMnghRUvYJqmv0OSAs6YCF6zZg3JycnEx8f7tH/66ac4nU5uvvlmUlNTmTt3Lq+//jput5vJkyeXasAi56p9+/ZceeWV/Prrr3zxxRcVOhncseO3pHTfQseO3yoJLF5ut9s7E/hUp2sXERH/0RhbREREKrPk6GQeaPkAX+/4mi+2fuHvcKSAMyaCd+/eTdOmTQu1L1++HJvNxuOPP45hGABcd911NGvWjNWrV5dOpCIX4IorrqBr1678/PPPzJ49u8Img0UK2rt3LwsWLOCNN944Y7///e9/rFq1iszMzDKKTEREzkRjbBEREanshjQaQpv4Nryy6hV2Htvp73AkzxkTwYcOHSI6OtqnLTMzkz///JNGjRoVupytSZMm7N27t+SjFCkBXbp0oVOnTqxevZp58+bp8gSpkI4dO8Z3333He++9x3vvvcfKlStJTEykXbt2hRZEtNlsNGvWDNM0mTdvHq+//jqTJ09mw4YNFbpmtohIRacxtoiIiFR2VouVlzq+hNWw8syyZ3C6nf4OSQDbmTYahsGxY8d82jZu3IhpmjRq1KhQ/4iICJxOfbBSPhmGQffu3XE6naxYsQKr1UqPHj28M25Eyqvc3Fw2bNjAunXr2Lp1K6ZpkpSUxLXXXkuTJk0IDQ0FICkpidTUVDIyMoiMjCQlJYVmzZoBkJ6ezs8//8y6devYsGEDISEhNG3alBYtWhAfH69/B/L/7N15fBT1/cfx18zsmfsmF4TLhDMJhwgqVRu8j1qvav15VXvZw2pP29pa+2vtZautrba2tbVaD1T4FY9iS6viLSgJNwgIJCSQkINce83M74/Z3exmNyFAkl3g8/QRd/c73539bljI5L2f/YwQYhTJMbYQQgghjgdFaUV8Z/53+NbKb/HndX/mM5WfSfSSjnuDBsGFhYWsXbs2amzVqlUoisKMGTNi5re1tZGXlze8KxRiGCmKwllnnYWu67z11lvYbDZqamokBBNJxzAMdu7cSW1tLRs2bMDn85GZmcmpp55KZWUl+fn5MfeprKwMB7/9FRYWUlhYyKJFi/jggw+ora1l1apVvP322xQUFFBdXU1lZSVpaWkj/dSEEOK4J8fYQgghhDhenD/xfF7Z/QoPrHmAU4pPYXre9EQv6bg2aBA8f/58nnzySZYuXcrFF1/Mvn37ePLJJ1EUhYULF8bM37RpU8xJL4RINoqicO6556LrOq+99hqapnHGGWckellCANDc3Byu3D1w4AAOh4Pp06dTWVlJWVkZqjpoR5+D0jSNiooKKioq6OnpYd26ddTW1vLSSy/xr3/9i8mTJ1NdXU1FRUVMqwkhhBDDQ46xhRBCCHE8+c787/Devvf41spv8dSFT+G2uRO9pOPWoL/l33jjjfzf//0ft99+Oz/+8Y/p6ekhEAhw7rnnMmbMmKi5LS0trF27lhtuuGFEFyzEcFAUhfPPPx9d13nllVew2Wxxf/ESYjR0d3eHA9k9e/agKAqTJk3izDPPpKKiAofDMSKPm5KSwrx585g3bx7Nzc2sWbOGuro6Fi9ejMvlYsaMGVRXV1NSUiJV80IIMYzkGFsIIYQQx5NMZyY/OvVH3PTSTdyz6h6+O/+7iV7ScWvQIHjs2LH8/ve/54477mDnzp3YbDbOOecc7rrrrpi5Tz75JIZhcPLJJ4/YYoUYTqqqctFFF6HrOitWrEDTNHn9ilHj9/vZsmULtbW1fPDBBxiGQWFhIWeffTYzZswgPT19VNeTn5/PmWeeSU1NDdu3b2fNmjWsWbOGVatWkZeXR1VVFVVVVWRkZIzquoQQ4lgkx9hCCCGEON6cVHQS1067lkc2PMJHSj/CR0o/kuglHZcU0zTNoUxsbW0lLS1twMq03t5eAoEAaWlpR3XlWH19PTU1NaxYsYLS0tJEL0eMAl3XeeaZZ9iwYQPnnnsuJ510UqKXJI5Rpmmya9cu6urqWLduHV6vl/T0dGbOnElVVVVMFViieTwe1q9fz5o1a9i9ezeKojBx4kSqq6uZMmUKdrs90UsUQogRM1rHhMN5jC3HsUIIIYRIZl7dy1XPX0VrbyvPfuxZclw5iV7SMedgx4NDbgCZkzP4H47bLf09xNFJ0zQuvfRSdF3nxRdfRNM05s6dm+hliWPI/v37qauro66ujra2Nux2O1OnTqWqqooJEyYccd/fkeJyuZgzZw5z5sxh//791NbWUltbyzPPPIPT6WT69OlUVVUxbty4o/oNQCGESCQ5xhZCCCHE8cKpOfnJwp9w5XNX8oM3fsC9Z9wrv0uOMjkTkBBYYfDll1/Ok08+yXPPPYfNZqO6ujrRyxJHsZ6eHtavX09tbS319fUATJw4kdNPP50pU6bgdDoTvMJDk5uby0c/+lFOP/10du7cyZo1a1i7di3vvfceOTk54dYRWVlZiV6qEEIIIYQQQogkVZ5dzi2zb+EXq37Bkg+WcMkJlyR6SccVCYKFCLLZbFxxxRU8/vjj/N///R+apjFz5sxEL0scRQKBAB988AG1tbVs2bIFXdfJz89n0aJFzJw5k8zMzEQv8YipqsqECROYMGEC5513Hhs3bmTNmjX897//5b///S/jx4+nurqaqVOnHnVhtxBCCCGEEEKIkXfNtGtYWb+Sn7zzE04ccyJjM8YmeknHDQmChYhgt9u58soreeyxx3j22WfRNI1p06YlelkiiZmmSUNDA7W1taxbt47e3l5SU1M58cQTqaqqorCw8Jj9qIvT6aS6uprq6mra2tqoq6tjzZo1LF26lOeff55p06ZRXV1NWVlZ0ra/EEIIIYQQQggxulRF5X9P/V8u+cclfOu1b/HXc/6KTZWIcjTId1mIfhwOB5/85Cd59NFHefrpp7niiiuYMmVKopclkkwo+Kyrq2P//v3YbDamTJlCZWUlkyZNQtO0RC9xVGVnZ3PaaafxkY98hF27doWD8draWjIzM8OtI3JzcxO9VCGEEEIIIYQQCVaYWsgd8+/gG69+gz+u/SOfq/pcopd0XJAgWIg4nE4nV199NY888giLFy/myiuv5IQTTkj0skSCeTweNmzYQG1tLTt37gSgrKyMU045hWnTpuFyuRK8wsRTFIWysjLKyso455xz2LRpE7W1tbz66qu8+uqrjBs3jqqqKqZPny7fLyGEEEIIIYQ4jp074VxeqX+FB2sf5JTiU5iZL+05R5oEwUIMwOVycc011/DXv/6VJ554gquvvpqJEycmellilOm6zrZt26itrWXz5s0EAoHwidNmzpxJdnZ2opeYtBwOB5WVlVRWVtLR0UFdXR21tbUsW7aMF198kalTp1JVVcXEiROldYQQQgghhBBCHIe+fdK3Wb13Nbe/djtPXfAUKfaURC/pmCZBsBCDcLvd4TD473//O//zP//D+PHjE70sMcJM06SxsZG6ujrWrl1Ld3c3brebWbNmUVVVRUlJyTHb93ekZGZmsnDhQk499VQaGhpYs2YN69atY+3ataSnp4dbR+Tn5yd6qUIIIYQQQgghRkmGI4Mfn/pjblx+I/esuoc7FtyR6CUd0w45CG5tbaWxsZG2tjZcLhe5ublyIiBxTEtNTeXaa6/lL3/5C3//+9+55pprGDtWzmh5LDpw4EC4arW5uRlN0ygvL6eqqorJkydjs8l7Z0dKURRKS0spLS3l7LPPZsuWLaxZs4bXX3+d1157jZKSEqqrq5k+fTopKfJOsBDi+CHH2EIIIYQ4Xp1YeCLXT7+eh9c/zEdKP8JpY09L9JKOWUNKNbZt28bixYt57bXX2LZtW8z21NRU5s6dyznnnMN5552Hw+EY9oUKkUhpaWnhMPjRRx/l2muvpaSkJNHLEsPA6/WyceNG6urq2L59OwBjx47lggsuYNq0aRJGjiC73c706dOZPn06nZ2drF27ljVr1vD888/zz3/+k4qKCqqrq4/Lk+8JIY4PcowthBBCCGH54qwv8saeN/jeG9/j2YueJdctJxofCYppmuZAG+vq6vjlL3/J22+/jWma2Gw2Jk+eTF5eHpmZmXi9Xtrb29m+fTutra0oikJWVhbXXnstN95441F5sFpfX09NTQ0rVqygtLQ00csRSaajo4OHH34Yj8fDddddR1FRUaKXJA6DYRjs2LGD2tpaNm7ciN/vJysri6qqKiorK8nNlR84iWKaJk1NTaxZs4a1a9fS09NDamoqlZWVVFdXM2bMmEQvUQhxnBjJY8KROsaW41ghhBBCHM22tm3lyueu5OTik/n1R38tLRkPw8GOBwesCP7Wt77FP/7xD1JSUrj00ks5//zzqa6uxu12x52/a9cuXn31VZYsWcJ9993H4sWL+dnPfsbcuXOH79kIkWCZmZlcd911PPzwwzzyyCNcf/31EkwdRfbu3UttbS1r166ls7MTl8tFZWUlVVVVjB07Vn7IJAFFUSgqKqKoqIgzzzyTDz74gDVr1vD222/z5ptvUlhYSHV1NTNnziQ1NTXRyxVCiEMmx9hCCCGEEPGdkH0CX5nzFX727s94ZuszXFZ+WaKXdMwZsCL4pJNO4qabbuKaa67B5XId0k7Xrl3Lvffey+zZs/nCF74wLAsdLVJJIYZi//79/OUvf8EwDK6//no5wVUSC7UcqKuro6mpCVVVOeGEE6isrKS8vBy73Z7oJYoh6O7uZt26daxZs4bGxsbwn2N1dTUnnHCC9G8WQgy7kTomHMljbDmOFUIIIcTRzjANPvuvz1LbXMviCxdTllGW6CUdVQ52PDhgENzV1UVaWtoRPfhw7GO0yQG0GKrm5mb+8pe/oCgKN9xwg7QTSCI+n4/NmzdTW1vLtm3bME2T4uJiqqqqmDFjhlSSHuVCld21tbV0d3fjdruZOXMm1dXVFBUVSWW3EGJYjNQx4UgeY8txrBBCCCGOBXu793LJPy5hfMZ4/nruX7GpUvgzVIfdGmI4AtyjLQQW4lDk5+dz3XXX8Ze//IW//vWv3HDDDWRnZyd6WcctwzDYuXMntbW1bNiwAZ/PR0ZGBqeeeiqVlZVStX0MGTNmDGeddRY1NTVs27aN2tpaVq9ezTvvvENBQUG413N6ejp1dXWsWLGCjo4OMjMzqampobKyMtFPQQhxHJNjbCGEEEKIwY1JHcMdC+7g6698nYfqHuLz1Z9P9JKOGRKpC3EECgoKuPbaa8Nh8PXXX09WVlail3VcaW5upq6ujrq6Ojo6OnA4HEybNo2qqirKyspQVTXRSxQjRNM0ysvLKS8vp7e3l3Xr1lFbW8u//vUv/v3vf1NQUEBLSwu6rgPWyR6XLVsGIGGwEEIIIYQQQiSxc8afw6u7X+X3db/nlJJTqMyX3+GGwyEHwS+++CLPPPMMGzZs4MCBA2RkZDB9+nQuvfRSzjnnnJFYoxBJrbCwkGuuuYZHHnkkXBmckZGR6GUd00L9Ymtra9mzZw+KojBp0iQWLVpERUXFgGdTF8cut9vNiSeeyIknnkhLSwtr1qzh9ddfp3/3I7/fz4oVKyQIFkIkHTnGFkIIIYSIdvtJt7N672puX3k7iy9cTIo9JdFLOuoNOQjWdZ3bbruNl156KfyLtdvtprW1lZUrV/Laa6/xz3/+k3vuuQdN00ZswUIko5KSEv7nf/6Hv/3tbzzyyCNcf/318rHNYeb3+9myZQt1dXVs3boVwzAoLCzk7LPPZsaMGaSnpyd6iSJJ5OXlsWjRIl577bW42zs6Onj66acZN24c48aNo6CgQCrHhRAJI8fYQgghhBDxpTvS+dGpP+JTyz/Fz979GXeefGeil3TUG3IQ/Kc//Ynly5czZcoUvvKVrzBv3jxSUlLo6enh7bff5r777mP58uVMnz6dT3/60yO5ZiGS0tixY7n66qt59NFHeeSRR7juuuvkpGRHyDRNdu/eTW1tLevXr8fj8ZCWlsb8+fOpqqpizJgxiV6iSGKZmZl0dHTEjNvtdnbu3Mm6desAcDqdjB07NhwMl5SUYLfbR3u5QojjlBxjCyGEEEIMbG7hXG6YcQN/XvdnTis9jTPGnZHoJR3VFLP/52YHcPbZZ+P3+3n++edxu90x27u7u7nwwgux2+0sX7582Bc6WuRsy+JIbd++nb///e/k5eVx7bXXkpIiH104VK2trdTW1lJXV0dbWxt2u52pU6dSVVXFhAkTpHpTDEldXR3Lli3D7/eHx+x2OxdeeCEzZ86kvb2dXbt2hb+am5sBUFWV4uJixo0bR1lZGWPHjpW/x0Ich0brmHA4j7HlOFYIIYQQxyK/7ueTL3ySfT37eOaiZ8hz5yV6SUnrYMeDQ64I3rNnD5/85CfjHqACpKamsmjRIp544onDX60Qx4CJEydy5ZVX8vjjj/Poo49y7bXX4nK5Er2spNfb28v69eupra1l9+7dgPW9PO2005g6dSpOpzPBKxRHm1Af4BUrVtDR0UFmZiY1NTXh8ezsbLKzs6mqqgKgp6eH3bt3h4Pht956izfeeAOA/Pz8cMXwuHHjyMrKQlGUxDwxIcQxRY6xhRBCCCEGZ9fs/GThT/jEc5/g+298n/s/er/8PnaYhhwE5+TkHLQKT1VVcnJyDnkRb7/9Nvfffz/r1q1D0zSqq6u57bbbmDZt2iHvq6uriwsvvJA9e/Zw3nnn8atf/eqQ9yHEkZo8eTJXXHEFTz75JI8++ijXXHONBJlxBAIBPvjgA2pra9myZQu6rpOfn8+iRYuYOXMmmZmZiV6iOMpVVlYO+cRwKSkpVFRUUFFRAVh9qffs2RMOhtetW8fq1asBSE9PjwqGx4wZI5XqQojDMpLH2EIIIYQQx4pJWZO4dc6t/OSdn7B4y2KuqLgi0Us6Kg05CD7zzDNZsWIFt912W9zeiT6fjxUrVrBo0aJDWsDLL7/M5z//edLT0/n4xz8OwHPPPcdVV13F3/72t0M+s/svfvEL2tvbD+k+QoyEiooKLrvsMhYvXszf//53rr76ahwOR6KXlXCmadLQ0EBtbS3r1q2jt7eX1NRUTjzxRCorKykqKpJ39kRSsNvtlJWVUVZWBoBhGDQ3N4eD4Z07d7J+/XoAHA5HTJ9h+fsuhBiKkTrGFkIIIYQ41lw15SperX+VX6z6BfMK5zE+c3yil3TUGXKP4K6uLj71qU+haRpf/epXmTNnDoqiYJomq1ev5p577kHXdR5++OEhnyDL5/Nx5pln0tHRwbPPPsvEiRMB2LZtG5deeikTJ07k2WefHfKTeffdd7nmmmv41re+xd13331YFcHSW00Mt7Vr1/Lss88yfvx4PvnJTx63J6Fqb2+nrq6O2tpa9u/fj6ZpTJkyhaqqKiZNmiRnQhdHpf59hvft2wdY1XtFRUVRVcNy8kghji6jdUw4nMfYchwrhBBCiGPdvp59XPKPSxibNpZHznsEu3p8ZiwDGbYewR/72Mfw+/00NzdzzTXXoGka2dnZtLW1oes6YPVQvOiii6LupygK//73v+Pu84033qCpqYlPfOIT4RAYYNKkSVx44YU89dRTbNiwYUgtIjweD9/97ndZtGgRixYt4u677x7qUxNiRM2cORNd11m6dClPPvkkV155JTbbkP/qHdU8Hg8bNmygtraWnTt3AlBWVsYpp5zCtGnTpHeyOOplZWWRlZUV/vRKb29vVJ/hd955hzfffBOA3NzcqGA4JydHqt+FECNyjC2EEEIIcawqSCnge/O/x1df+Sq/r/09X5z1xUQv6agy5DSqoaEh6nYgEAifYT0kVAk1VKtWrQJgwYIFMdsWLFjAU089xapVq4YUBN933320tLTwyCOPRJ0hXohkUF1dja7rLFu2jMWLF3P55Zcfs2Gwruts376d2tpaNm3aRCAQIDc3lzPOOIPKykqys7MTvUQhRozb7aa8vJzy8nLA+lkZ2Wd448aNvP/++4B1AqjIYLiwsFAq44U4Do3EMbYQQgghxLHsrPFncVH9RTy09iFOLTmV6oLqRC/pqDHkJGrTpk3D/uC7du0CYNy4cTHbQmOhOYOpq6vjr3/9K9/97ncZM2YM9fX1w7tQIYbBnDlz0HWdF154gWeeeYbLLrvsmAl9TNOkqamJ2tpa1q5dS3d3N263m1mzZlFVVUVJSYlUPorjks1mCwe9YPUZbmlpiWonsXHjRsDqSVxaWhqeX1paKieZFOI4MBLH2EIIIYQQx7rb593O6r2r+fZr32bxhYtJtUsrvqFIaEliV1cXAGlpaTHbQmOdnZ2D7sPv9/Od73yHqqoqrrrqquFfpBDDaN68eei6zvLly1myZAmXXHLJQc8UnswOHDhAXV0ddXV17Nu3D03TKC8vp6qqismTJx+zVc9CHC5VVSkoKKCgoIC5c+cC0NHREdVO4pVXXgGsj30XFhZGVQ2np6cncvlCCCGEEEIIkRTSHGn8+NQfc8PyG/jZuz/jByf/INFLOioc9SnN73//e3bs2MHSpUul4lAcFRYsWEAgEGDFihVomsbHPvaxoyoM9nq9bNq0idraWrZv3w5AaWkp559/PtOnTyclJSXBKxTi6JKZmUlmZiYzZswArN7a9fX14WB49erVvP322wDk5OREBcO5ubnys08IIYQQQghxXJo9ZjafmvEp/rj2j3yk9CPUjKtJ9JKS3iEHwf/+97958sknWb9+PQcOHCA9PZ0ZM2bwiU98gkWLFh3SvkJVv6HK4EihscGqn7Zv386DDz7IZz7zGSZPnnxIjy1EIi1cuBBd13n55ZfRNI0LLrggqcNgwzDYsWMHtbW1bNy4Eb/fT1ZWFqeddhqVlZXk5uYmeolCHDNcLheTJ08O/1wLBAI0NTWFg+EtW7awZs0aAFJSUmL6DEslvhBHp+E8xhZCCCGEOF7cXHUzrze8zg/e+AFV+VXkufMSvaSkdki/LX7ve99j8eLFmKYJWCfFaWtrY+XKlbz22mtcdtll/PCHPxzy/iL7AE+fPj1q22D9g0O2b9+O3+/nt7/9Lb/97W9jtr/wwgu88MIL1NTU8Lvf/W7I6xJiNJx22mkEAgFee+01NE3jvPPOS7rKvr1794ZbP3R2duJ0OqmsrKSyspJx48Yl3XqFOBbZbDZKS0spLS3l5JNPxjRN9u/fH9VnONRjNDQ3ss+wy+VK8DMQQhzMcB9jCyGEEEIcL+yanZ8s/AlXPHcFd7x+B7+r+Z1kFYMYchC8dOlSnnrqKcaPH89tt93GySefTFpaGl1dXbz55pv88pe/5Omnn2bOnDlcfPHFQ9rn3Llzeeihh3jzzTc599xzo7a9+eab4TkDKSkp4bLLLosZ7+np4YUXXmDs2LGcdNJJTJs2bahPU4hRoygKNTU16LrOm2++ic1m46yzzkr4P1idnZ2sW7eO2tpampqaUFWVyZMnc84551BeXo7dbk/o+oQ43imKQl5eHnl5ecyePRuw/t5GBsMrV67ENE0URWHMmDFRVcMZGRkJfgZCiEgjcYwthBBCCHE8mZg1ka/O/So/fvvHPLn5Sa6ccmWil5S0hhwEP/nkk+Tl5fHUU09F/RKZlpbGmWeeybx58zj//PN58sknh3yQevLJJ1NYWMg//vEPrr/+eiZOnAjAtm3bWLZsGdOmTQuHuH6/n127duF2uykuLgZg6tSp/OhHP4rZb319PS+88AIzZ86Mu12IZKEoCmeddVY4DNY0jZqamlEPg/1+f7jv77Zt2zBNk+LiYs4991xmzJhBaqqcfVOIZJaens706dPDn67xer1RfYbff/993nnnHQCysrKiguG8vLykbk0jxLFuJI6xhRBCCCGON1dWXMkr9a9wz6p7mFc0j4mZExO9pKQ05CB4y5YtXHzxxQNWEmVmZnL22WezZMmSIT+4w+Hgzjvv5Oabb+bKK6/kggsuAOC5557DMAx+8IO+M/7t3buX8847j3nz5vG3v/1tyI8hRLJTFIVzzz0XXdd57bXXsNlsnH766SP+uIZhsHPnTurq6li/fj0+n4+MjAxOOeUUqqqqyM/PH/E1CCFGhtPpZNKkSUyaNAkAXdej+gxv27aNuro6wPoI+tixY8PBcHFxsfQZFmIUjcQxthBCCCHE8UZRFH548g+55B+XcPvK23n03Eexa/KJ5v6G/JteIBA4aJ9Bl8uFruuHtIAzzjiDhx9+mN/+9rcsWbIERVGYPXs2t956a0zfYCGOVYqicP7550edQG7hwoUj8lgtLS3U1tZSV1dHR0cHDoeDadOmUVVVRVlZmVQGCnEM0jSNkpISSkpKWLBgAaZp0traGtVOYsuWLVFzQ8Hw2LFjcbvdCX4GQhy7RuoYWwghhBDieJOfks+dC+7kKy9/hQdqH+DLs7+c6CUlnSEHwWPHjmXlypV8/etfj7vdNE1ef/11SktLD3kR8+fPZ/78+YPOKS0tZfPmzUPa36HMFSJZqKrKRRddhK7rrFixApvNxoIFC4Zl393d3axbt466ujoaGhpQFIVJkyZRU1PDlClTcDgcw/I4Qoijg6Io5Obmkpuby6xZswDo6upi9+7d4WD4jTfe4LXXXgOgoKAgqp1EVlZWAlcvxLFlJI+xhRBCCCGONzVlNXx88sf507o/sbB0IbMKZiV6SUllyEHw2WefzW9/+1u++tWv8s1vfpOCgoLwtpaWFn7+85+zefNmbr755hFZqBDHA1VVufjii9F1neXLl6NpGvPmzTusfQUCAbZs2UJtbS1bt27FMAzGjBnDWWedxcyZM0lPTx/m1QshjmZpaWlMnTqVqVOnAuDz+WhoaAgHw3V1daxatQqAjIwMysrKwsFwfn6+fJpAiMMkx9hCCCGEEMPrm/O+ybtN73L7ytt5+sKnSXOkJXpJSWPIQfCNN97Iv//9b55//nn+9a9/MWnSJPLy8mhpaWH79u14vV4qKiq48cYbR3K9QhzzNE3j0ksvRdd1XnjhBTRNY86cOUO6r2ma7N69m9raWtavX4/H4yEtLY358+dTWVlJYWHhCK9eCHGscDgcTJgwgQkTJgBWn+F9+/aFg+EdO3awdu1awOpJHFkxXFxcjN0u/biEGAo5xhZCCCGEGF6p9lTuXng31/3zOn767k/54Sk/TPSSksaQg+CUlBQee+wx7rnnHpYuXcrGjRvD29xuN1dddRVf/epXSUlJGZGFCnE80TSNyy+/nCeeeIJly5ahaRrV1dUDzm9tbaWuro7a2lra2tqw2+1MmTKFqqoqJk6cKJV6QogjpmkaRUVFFBUVcdJJJ2GaJu3t7VF9hrdu3RqeW1xcHNVnWI4PhIhPjrGFEEIIIYZfdUE1N828iT/U/YHTSk9jUdmiRC8pKSimaZqHeqdAIMD27dvp7OwkPT2diRMnHjNnGK+vr6empoYVK1ZILzaRcH6/n8cff5wdO3Ywd+5ctmzZQkdHB5mZmeGTydXW1rJ7924AJkyYQFVVFVOnTsXpdCZy6UKI41BPTw+7d+9m586d7Nq1iz179mAYBgD5+fkxfYYVRUnwioUYWCKOCY/0GFuOY4UQQggh+vgNP9e8cA31XfU8e9GzFKQUHPxOR7mDHQ8e9MjSMAyef/55amtrAZg1axbnnnsu5eXlw79aIUQUu93OlVdeye9//3vefffd8HhHRwfPPfccYIUrixYtYubMmWRmZiZqqUIIQUpKChUVFVRUVADWm1mRfYbXrVvH6tWrAUhPT48KhseMGSOfXhDHFTnGFkIIIYQYWXbVzt0L7+aKZVfwvde/xwOLHjjui1EGDYJ9Ph833HAD7733HmD1H33sscd44okn+POf/yz9/4QYBQ6HA7/fH3dbWloaN99883H/D5kQIjnZ7XbGjx/P+PHjASv4iuwzvGvXLtavXw9Y/9aNHTs2HAyXlJTgcDgSuHohRo4cYwshhBBCjI4JmRP42tyv8b9v/y+Pb3qcT079ZKKXlFCDBsGPPvooq1evJjs7m7PPPhvTNHnppZdYtWoVjz32GNdff/0oLVOI49uBAwfijnd1dUkILIQ4aqiqSmFhIYWFhcybNw8gps/wf//73/DcoqKiqKrh1NTURC5fiGEjx9hCCCGEEKPniooreKX+FX65+pfML5rPxKyJiV5SwgwaBP/zn/8kNTWVJUuWUFhYCMBnP/tZLrjgAl588UU5SBVilGRmZtLR0RF3XAghjmZZWVlkZWVRWVkJQG9vL7t37w4Hw++88w5vvvkmALm5ueFQuKysjOzsbHkzTByV5BhbCCGEEGL0KIrCXafcxSX/dwnfWvktHjvvMeza8fkJrEGD4B07dnDmmWeGD1ABiouLWbRoUbhiRwgx8mpqali2bFlUiwi73U5NTU0CVyWEEMPP7XZTXl4e7pPq9/tpbGwMB8MbN27k/fffB6z2OP37DGualsjlCzEkcowthBBCCDG68tx53Hnyndzy31v4Xe3vuGX2LYleUkIMGgR3dXVRXFwcM15SUkJ3d/eILUoIES1UKbdixQo6OjrIzMykpqYmPC6EEMcqu90eDnrB6jPc0tIS1U5iw4YN4bn9+ww7nc5ELl+IuOQYWwghhBBi9H103Ee59IRL+dPaP3FqyanMGTMn0UsadYMGwaZpxj2Dt6ZpmKY5YosSQsSqrKyU4FcIcdxTVZWCggIKCgqYO3cuAB0dHVHtJF5++WXA+ghYZJ/hsWPHkp6ensDVC2GRY2whhBBCiMT4xonf4J2md/j2ym/z9EVPk+44vn4/GDQIBqT3nhBJ4pmmVu7e3kiD10+J087tE4u4tDAn0csSQoiEy8zMJDMzkxkzZgDg8XiiguFVq1bx1ltvAZCTkxPVTiI3N1eOdURCyOtOCCGEEGL0pdhTuHvh3Vz34nX85J2f8KNTf5ToJY2qgwbB999/P/fff3/cbVOnTo0ZUxQl/BFNIcTweKapla9t3k2vYVUJ1Xv9fG3zbgKmyRWFOfLLpBBCRHC5XJxwwgmccMIJAAQCgag+w5s3b2bNmjUApKSkRAXDRUVF0mdYjAo5xhZCCCGESIyq/Co+XflpHqx9kNNKT+Os8Wclekmj5qBB8KF+PE0+zibE8Lt7e2M4BA7pNUxu2bSbWzbtxqaAXVGwqwo2RcGhqNhUrEtFwREaD29XsKn9LiO221XF2t9A1/tfxlxXsSsELweepylSESVGllTSCwCbzcbYsWMZO3Ysp5xyCqZpxvQZ3rRpU3huaWlpOBguLS3F5XIl+BmIY5EcYwshhBBCJM5nKj/D6w2vc9dbd1GVX8WY1DGJXtKoGDQIDv1SJIRIrAavf8BtXx0/hoAJPsMgYJr4DNO6NE0CRvAyYtxvmHQaBn6/id8MfhlxrgfvO5IUGGKwrEQH3fEC7ajAW8WmBIPwQQLvoQbj/efZFQU1CQNsCT2jDVRJDxzX3xdhvQGVn59Pfn4+c+ZYJ4jo7OyMCoZXrlyJaZooisKYMWOiqoYzMjIS/AzE0U6OsYUQQgghEsuu2rl74d1cvuxy7nj9Dh4880FUJfYcDseag1YECyESr8Rppz5OGFzqtPP1CUUj9rimaaKbHCQwNvCbBC8HD5YH3Ba8PmCQHbz0BkPs6HkGAQPrMmKfgREunNIUwoFxbJCtYlexLg+pkjp2bKgB9cq2Tn69ax/eiNDzts27afL6OSsvExMIfUvM4LXInN+MuAxVnUWNha6bkWN9NyLnHtp+wnuJ2X//x4l+7H7zzOj9AHzvg4a4lfQ/3t4oQbCIkZ6ezvTp05k+fToAXq+X+vr6cDD8/vvv88477wCQlZUVFQzn5eXFPfGXEEIIIYQQInmVZZTxtblf44dv/ZDHNz3O1VOvTvSSRpwEwUIcBW6fWBRV2QjgVhVunzhyITBYVXM2BWwouEf0kYafYZpRwfCRBNSDz+sXhA9w3y7DwB84SOAdHB+uDNtrmPxweyM/3N44THs8NjR4/Ux7bS35DjsFDhsFDjv5wcvI2/kOOzl2LSmrv8XIczqdTJo0iUmTJgGg6zpNTU3hYHjbtm3U1dUB4Ha7Y/oM22xyiCWEEEIIIUSyu7z8cl6tf5VfrvolJxWexOTsyYle0oga8LeU1tZWcnKOrGJqOPYhhOj7GLt87H/o1GClrOMoLNLTI4Pig7T6sFqCwCdqtw24vwenlYWvhzJNBSV4CUrMtoix0Dylb4yo+8TZT8T+Ih8nPK9frqoM8DjR94mzn36LUcJ7sfZzTd129vkC9JehqVyYn0WzL8A+n59VHd3s8/ljqofBqvzOt1sBcb7DToEzMiiODo/TNFV6Xh/DNE2jpKSEkpISFixYgGmatLa2RrWT2Lx5M2D1GS4pKYnqM+x2H21vp4mRIsfYQgghhBDJQ1EU7jz5Ti79x6Xc/trtPHbeYzg0R6KXNWIGDIIXLVrE9ddfzw033EB6evoh7fStt97i3nvv5dRTT+WLX/ziES9SCGGFwRL8Hh80RcGtHVqgWDpI+5CLx2QP19KOKt+fVBy3kv7u8tKYv0umadKtG+wLhsOhy1BYvM8boNnvZ0N3L80+f9zWI25ViaoyzusXFBc4bOQ77eTbbbi0o/AdChFFURRyc3PJzc1l1qxZAHR1dbF79+5wMPz666+zcuVKgJg+w5mZmYlcvkggOcYWQgghhEguee48fnDyD/jSf77E/Wvu57Y5tyV6SSNmwCD44x//OL///e/585//TE1NDeeffz6zZ88mKysrZq5pmmzevJmVK1eydOlStm/fzoQJEzjttNNGcu1CCCGCEtU+JJkdSiW9oiik2TTSbBoTU5yD7tcwTdr8enRQHLxsCV5u7/XyVkcXrX497j4ybRoFDltMWNy/yjjXYUOTKuOjRlpaGlOnTmXq1KkA+Hw+Ghoa2LlzJ7t27aK2tpZ3330XgMzMzKhgOD8/H1VVqaurY8WKFXR0dJCZmUlNTQ2VlZWJfFpimMkxthBCCCFE8jl97OlcVn4Zf1n3FxaWLOTEwhMTvaQRoZihs/nEsWXLFn71q1/xyiuvhE/6U1JSQn5+PpmZmXi9Xtrb29m5cye9vb2YpklhYSGf+tSn+OQnP3lU9serr6+npqaGFStWUFpamujlCCHEkD3T1CrtQ5KM3zBp8QeDYq8VHEeGx80RIXKXbsTcXwVyHTby7cGg2DlwcJxp06Q1RZLTdZ29e/dGtZPo6uoCwOVykZmZSXNzM4bR91qw2+1ceOGFEgYnwEgeE47UMbYcxwohhBBCHL4efw9XPHcFPt3HMxc9Q7rj0D69lQwOdjw4aFJbXl7OAw88QH19PYsXL+aNN95gw4YN1NfXR83Lzc3ltNNO45xzzmHRokVomja8z0IIIcRBSfuQ5GNXFYqcDoqcDjjIMUS3rgcriqMD4sj2FFt7PDT7AvjivIfrUJTwSe6igmJn/5Pg2UiVn9MJoWkaxcXFFBcXM3/+fEzTpK2tLRwKr1mzJioEBvD7/axYsUKC4GOMHGMLIYQQQiSfFHsKd596N9e8eA0/fvvH3L3w7kQvadgNqWS3tLSUW2+9lVtvvRWPx8PevXtpa2vD5XKRm5tLfn7+SK9TCCGEOKalahqpbo0y9+CtKUzTpCOghwPj/u0pmn0BGrw+3u/sYb8vQGydMaRq6oDtKCLD5DyHDYcq/YxHiqIo5OTkkJOTQ3V1Ne+9917ceR0dHaO8MjFa5BhbCCGEECK5zMyfyWerPsvv1vyO00pP45wJ5yR6ScPqkHs3uFwuysrKKCsrO/hkIYQQQgwrRVHIstvIstsoT3UNOlc3TVr9gXBripgqY1+Azd0eXmvroj0Qv59xjl2LrTLuFyDnO+zk2DVUaU1xRDIzM+OGvnJiueODHGMLIYQQQiSHT8/8NK81vMZdb91FdUE1hamFiV7SsDn6mvgKIYQQYkg0RSHfYSffYWd6mnvQuR7doMUf244idLvZF2BVRzf7fP6okxL2PRbk2/uC4QJnsLexM7bqOE1TpZ9xHDU1NSxbtgy/3x8es9vt1NTUJHBVQgghhBBCHF9sqo27T72by5Zdxndf/y5/OPMPqMqx8UlJCYKFEEIIgUtTKdUclLocg84zTZNu3QhXF0dWGUf2Nt7Q3Uuzz08gzilp3aoSrjLOj9OeosBhI99pJ99uw6UdGwdcQxHqA7xixQo6OjrIzMykpqZG+gMLIYQQQggxysZljOObJ36TO9+8k0c3PMq1069N9JKGhQTBQgghhBgyRVFIs2mk2TQmpgzez9gwTdr8elSVcf/LHb1e3u7ootUfvzVFpk0L9yvu38s48nauw4Z2DFQZV1ZWSvArhBBCCCFEErjkhEt4uf5l7nvvPhYUL+CE7BMSvaQjJkGwEEIIIUaEqijkOmzkOmxMPchcv2HS4vcHT4IXDIu9fVXHzT4/azt72efz06XHngJPBXLstr6g2DnwSfCybJq0phBCCCGEEEIMSlEUfnDyD7jk/y7hWyu/xePnP45DG/wTlMlOgmAhhBBCJJxdVShyOihyHvzAqkc3aO530rv+vY239nho9gXwmbG9KRyKEhUMhyuMncG2FBG9jVM1bSSerhBCCCGEEOIokOPK4a5T7uILK77Ab97/DV+d+9VEL+mISBAshBBCiKNKiqZS5nZS5h68NYVpmhwI6LFBccTtPV4/azp7aPEFiK0zhlRNjduOon9v4zyHDYd6/PQzFkIIIYQQ4njxkdKP8ImKT/DX9X9lYclC5hXNS/SSDpsEwUIIIYQ4JimKQqbdRqbdxgmprkHn6qZJqz9ghcTe2CrjZl+Azd0eXmvroj0Qv59xjl2LqjLu39c4FCDn2DXUAVpTPNPUyt3bG2nw+ilx2rl9YhGXFuYc8fdCCCGEEEIIcfi+OvervN34Nt95/Ts8c9EzZDgyEr2kwyJBsBBCCCGOe5qiBKt87UxPcw8612sYMSe92+cN0Oy3ehnv8wZY1dHNPp+fXiO2NYWmQL7dHnMSvD1eH//Y1xFuZ1Hv9fO1zbsBJAwWQgghhBAigdw2N3cvvJtrXriGH731I376kZ8mekmHRYJgIYQQQohD4FRVSl0OSl2D9zM2TZNu3YhbXRzZomJjt4dmn59AbGZMr2Fy9/ZGCYKFEEIIIYRIsBl5M/hc1ee4f839nFZ6GudNPC/RSzpkEgQLIYQQQowARVFIs2mk2TQmpgzez9gwTUperiVOFkyD1z8yCxRCCCGEEEIckhtn3sjKhpX871v/y6yCWRSlFSV6SYdEzmoihBBCCJFgqqJQ4rTH3TbQuBBCCCGEEGJ02VQbd596N7qp893Xv4thxjvldPKSIFgIIYQQIgncPrEItxp9Ejm3qnD7xKOrykAIIYQQQohj2diMsXxr3rd4p+kd/rbhb4leziGRIFgIIYQQIglcWpjDLyrGUuq0owClTju/qBgr/YGFEEIIIYRIMhdPvpiacTXc9959bG7dnOjlDJkEwUIIIYQQSeLSwhxWnTydxjOqWXXydAmBhRBCCCGESEKKovD9Bd8n05nJt1Z+C6/uTfSShkSCYCGEEEIIIYQQQgghhDgE2a5s7jr5Lj5o/4Bfv/frRC9nSCQIFkIIIYQQQgghhBBCiEO0sHQhV1ZcySMbHuGtxrcSvZyDkiBYCCGEEEIIIYQQQgghDsNtc29jQuYEvvPad+jwdiR6OYNKmiD47bff5pprrmHWrFnMnTuXm266iQ0bNgzpvhs2bOCXv/wll156KSeddBIzZ87k3HPP5d5776Wnp2eEVy6EEEIIIYQQQgghhDgeuW1u7l54N629rfzvW/+LaZqJXtKAkiIIfvnll7n++uvZvHkzH//4x7nooouoq6vjqquuoq6u7qD3v/POO3nooYdwOp187GMf46qrrkJVVR544AGuuuoquru7R+FZCCGEEEIIIYQQQgghjjfTc6dzc/XN/PPDf/L8jucTvZwB2RK9AJ/Px/e//32cTidPPPEEEydOBODqq6/m0ksv5c477+TZZ58ddB8f+9jH+OUvf0lpaWl4LBAIcOutt/LSSy/x2GOP8ZnPfGZEn4cQQgghhBBCCCGEEOL49KkZn2Jlw0p+/NaPmV0wm+K04kQvKUbCK4LfeOMNmpqauOiii8IhMMCkSZO48MILWb9+/UFbRFx99dVRITCAzWbjxhtvBGD16tXDv3AhhBBCCCGEEEIIIYQANFXjx6f+GAODz/3rc5z19FlU/rWSs54+i+e3J0eVcMKD4FWrVgGwYMGCmG2hsdCcQ2WzWQXPmqYd5uqEEEIIIYQQQgghhBDi4ErTSzl3/LnsOLCDxu5GTEwauxu58407kyIMTngQvGvXLgDGjRsXsy00FppzqJYuXQrA/PnzD29xQiSRpe83cMpP/sOEbz3PKT/5D0vfb0j0koQQQgghhBBCCCFEhNf3vB4z5tE93PfefQlYTbSE9wju6uoCIC0tLWZbaKyzs/OQ9/vOO+/w+OOPU1ZWxhVXXHFkixQiwZa+38Dtz9bR6zcAaGjv5fZn1wJw8aySRC5NCCGEEEIIIYQQQgQ1dTcd0vhoSngQPBK2bdvGl7/8ZZxOJ/feey8ulyvRSxJHMd0w8QZ0fAEDb8AIXuoR1/tfxs71BQy8uoHXb+CLutSjb/e7b+h6lzcQs65ev87XFtfy97d3keaykea0ke6ykeayke60ke6yk+bsdztintOmoihKAr6jQgghhBBCCCGEEMemwtRCGrsb444nWsKD4FDVb6gyOFJoLD09fcj72717N9dffz0ej4eHHnqIadOmDc9CjwNL32/g58s3s6e9l+IsN18/uyJh1aamaeLXzbhhaWQQO9QQtn9g6x1orh47VzfMI34+igIOTcVhU3HaNJw2FactdNu6dNs1Mt32mHGHpvHn13fE3W/AMNFUhX2dHrY3B+jyBjjgCeALGAddk11TIoJiezgwTnMFA2WnnfTwdVt4bkZkwOyy4bRJD24hhBBCCCGEEEIIgFtm38Kdb9yJR/eEx1yai1tm35LAVVkSHgRH9gGePn161LbB+gfH09DQwLXXXkt7ezsPPvggJ5544vAu9hhmtR5YS69fB6zWA996po7Wbi+nVxT0VaiGA1k9qrrVO0hgO7Sq2dD1YKWtbmAeef6KqmAFr3YVh6b2Xdq0cNgaqo6NHIu+7D+uBQNaa3/O8H7jPI5dw6Gp2DXliKpvl69voqG9N2a8JMvN45+J7YHtDeh0e3W6PAEOePx0eQN0eQJ0ev3By+BtjxUed3oCdHr8NB3w0NXct82nHzxQdmhqdEVy8DKyIjnNaSPDFbpuj5qXFqxWdtgS3rJcCCGEEEIIIYQQ4oicP/F8AO577z6aupsoTC3kltm3hMcTKeFB8Ny5c3nooYd48803Offcc6O2vfnmm+E5B7N3716uv/569u3bx69//WtOOeWUEVnvsepnyzeFQ+AQT8Dgruc2ctdzGw95f3ZNiQlM+wexKSm2uGHrUIPYyHnOOAGtQ1OxacdGuPj1syuignoAt13j62dXxJ1vVR1r5KQ6juhxvQE9JjC2Lv0RAXKArmDAHKpI3tPuocvbRafHT6cnQGAIVdUOmxpsYdEXHqc57REBcl97i3Rn/1YY9vB1+zHyZy6EEEIIIYQQQoij0/kTz0+K4Le/hAfBJ598MoWFhfzjH//g+uuvZ+LEiYDV53fZsmVMmzYt3N7B7/eza9cu3G43xcXF4X20trZyww030NDQwC9+8QtqamoS8lyORr0+nadX72ZPu2fAOfd+orqvTcFBKmedwSBWVaX37HAKtegY7dYdTpuGM00jN8152PswTTPc57jT01eZHLoeCpZDVcqR8xrae9kUEToPpU1HqMo7XJHcr3dyWsS29Ig2GJG3U50SKAshhBBCCCGEEOLYkvAg2OFwcOedd3LzzTdz5ZVXcsEFFwDw3HPPYRgGP/jBD8Jz9+7dy3nnnce8efP429/+Fh7/8pe/zLZt25g+fTrbtm3jN7/5TdRjlJSUcMkll4zOEzpKtHR5eeTNnfztzQ9p6/Fj1xTGt2/m5La3Sde76NTSeCP7JHrGViWsT7CIdvGskqPyz0JRFFx2DZddI28YAuUDHn9UYByqUu4KVh93ea3WF1aYbIXIu1p7+uZ5hxYou+wq6S57ODzua2XRr3dyuPVF9Mn40p12Up3aqFalJ1OfbyHE4ZG/x0IIIYQQQoiRkvAgGOCMM87g4Ycf5re//S1LlixBURRmz57NrbfeGtM3OJ49e/YAsH79etavXx+zfd68eRIEB21v7uKPr+3gmdX1eAMGi6aO4bOnTWTjyv+y5/lXsJsBADL0Lmr2v0LJgrIEr1gIS2SgXDD080fGME2TXr8ep1eyPzpYjuidHOqx/GFLT1RrjKGcR9Bt12Irkp32uL2TQyfg61+5nOa0oR2kyj5en+/bn10LICGSEEcJ+XsshBBCCCGEGElJEQQDzJ8/n/nzY096Fam0tJTNmzfHjP/nP/8ZqWUdM1bvbOX3r2znXxv3YtdULp1dwo2nTmRyQRoA7//8uXAIHGI3A/S+uQz+R0J0cexQFIUUh40Uh42CI9iPaZr0+PS4gXFnRMgcDpjD2/w0d3r7gmhvYEgnRkxxaBFVyMHeyc6+quSnV9fH9Pnu9ev88LkNFGe5sWtKuIe2w6Zij7h0Bi8PFjYfjaS68uhkmia6YaKHLg0Tw4CAYaCbfdcNg+AcA90gPDfyfrphYpgmAcPECN4OBMci50Q9lmkS0CPmmCa6bgYf27p/5HUj5jGx1mQSnGOtr/86+j/uxsYDMT3Ve/06P1++WV63QgghhBBCiCOWNEGwGH66YfKvDXv5w6vbeG9XO5luO188YzLXLhhPfnr0R/Q797fE3UdnSzPP3v19ik6YQlH5FIoml+NMSR2N5QuR1BRFIdVp9RMek3H4+zEMk55QhXJMr2R/9In6olpf+Gnq8ESdxC+e/d0+rvj9m0Nai6YqVmAcDIkdmoo9dBkxZgXISlSgHDmnb0wZMHSO3LfDpuDQNOw2JbwfZ7/72TUFRTm0oDpZqiuHK9SMCScHCTCjQsp480ch1IwJYAfYphuxax1KtX0i2VQFVVWwqQqa0nddDd7W1H5fMXPApqqoKthVFU1V0RQGPLHmnvbeUX6GQgghhBBCiGORBMHHII9f55n36vnjyh3saOmmNNvNnRdO44oTx5LiiP9Hnp6bR2dLc8y43eniQEszO2rfA9MERSG3ZCxFJ1RQdMIUik+oILd0HIoqJ9YS4nCoqhKu7C3MdB32fk7+yYq4J33MS3Nw7ydm4dcNvAEDv27gC12Gr5sxYz7dwB+6DI+Z+AI6vX6djt7I8Yh9hvanG0fybYlrsBDaESdgfu2DZjz+6HX0+nVuf3YtKzbtO/zqUAk1Bw01VSW4L1WxrmvWZeT9I/cXNT+03/6PG3F7aGslGK4qqKq1Pk0l+FjWWrV+jxVa66D7DW4bKaf85D80xAl9i7PcI/aYQgghhBBCiOOHBMHHkNZuH397cyePvPkh+7t9VJZmcv8nZ3HO9MKDnrAqf9yEmCDY5nBy5qe/wNSFZ+Dt6abxgy00bt1E49bNfPDuW6z7778AcLhTKJxcTvEJFcGq4Qrc6UdQIini6n5/HweWf4je7kXLcpJx9nhSZx1JcwNxLPnG2VOiql/B6k/83fOnceoJeaO+HtM0w4GwP9AvYI4Ijr3B4DgydI4JrMMhdLzx6GDbHzDp6fXjDxgxIXBIr19nXUMHajjAjAwOCYd+dnvyhJpRAWa8dfTfbxKEmuLQff3sirh/j79+dkUCVyWEEEIIIYQ4VkgQfAzYub+bP67cweLVu/H4DT46pYDPfGQiJ03IGdJHqXesWc32996hdNpMOvY10bm/hfTcPBZeeS1TF54BgDMllfGVsxhfOQuwQp62xj3BYHgTe7Zu5u0lizFNK3jJLiq22kmcMIWiEyrIHzceVdNG7ptwjOt+fx/tz27FDAZberuX9me3AkgYLIC+VgfJ0g9XURSr5YNNBefB54+EgaorS7Lc/Pdrp4/+goQ4iGT7eyyEEEIIIYQ4tkgQfBR7f1cbf3h1O/9c34RdVbl4VjGfXjiRE8akD3kfna0tvHj/PeSNG88lt9+J3TG0xEZRFHKKS8gpLmH6aTUA+Dy97N22lT1bN9O4dTMf1r7HhletE/nZnE4KJ50QbCdhhcOpWdmH/qSPUweWfxgOgUNMv8GB5R9KECzCLp5VIoFRBKmuFEcje+YaUiffR3p3E6mphdgzbwHk77UQQgghhBDiyEkQfJQxDJMVm/bx0KvbeefDVjJcNj5/2iSuP3k8BRmH1l/U0HVe+PUvCPh8XPCVbw45BB6Iw+Vm7PRKxk6vBKyq4QPNe4PB8CYat2xi9XNLeFe3QpmM/DEUl08J9huuoGD8RDSb/YjWcCwwDZPA/l78DV346rvw1Xeit3vjztXbvRz4zy7shanYi1PRMp2HfEItIY5VUl0pjjbPb3+eO9+4E49u9ftu7G7kzjfuBOD8iecncGVCCCGEEEKIY4EEwUcJj19nyfsNPLRyO9ubuynJcvO9C6wTwKU5D++P8c2n/079xnWc+4XbyC0ZO8wrtqqGMwsKySwoZOoppwHg93nZt2M7jVs20rh1M/Ub17Hp9VcA0Ox2xkyY3HciuvIppOeOfm/T0WSaJnqbF19DJ776Lvz1nfgaujA9Vliu2FXsxWkoTg3Tq8fuQIUDL+0M31RcNuxFKTiK0rAXpVoBcWEKil3acojjk1RJi2Tn0320edpo97bzs3d/Fg6BQzy6h/veu0+CYCGEEEIIIcQRkyA4ybX3+Hj0rZ385Y2dtHR5mV6cwX1XVnP+zKKDngBuMDvr1vDWkqeYfvoipn3ko8O44sHZHU5KKqZSUjE1PNa5vyXcZ7hxyybWvPQ8q59fCkBaTi5FJ1QE20lMoWDipCOuXE4k/YAX3+6uvuC3oROjO2Bt1BTsRamkVBfgKEnDXpqOvSAFRVNiegSDFRJnXXIC7mk5+Jt68Dd24W/sxt/YTfeqJkxfcK4Ctjy3FQwXpWIvSsNemIqW6ZDqYSGEGEaGadDp6wwHu22eNtq8bdZlxPXIbd3+7oPut6m7aRRWL4QQQgghhDjWSRCcBJa+3xDz0eU5Zdn86bUdPPnubnr9OqdX5POZhRNZMCn3iMO77vY2Xrj/F+SWjKXmhs8N07M4fOm5eaTnnkr5/FMB0AN+mj/cwZ6tm2gMtpXY+vYbAKiajYLxE8InoSsun0JG/pikDDT1Lh++hi78wfYOvvoujE6ftVEF+5hUXFNzcZSm4yi1wlnFFj/cD/UBPrD8Q/R2L1qWk4yzx4fHnWUZOMsywvNNw0Rv8+Bv7MYXDId99V301rWE56gptmDFcGpfSDwmFcV++G8wCCHEscSre6NC3HZP+6DBbru3Hd2M8+kNwKk5yXZlk+3MJtuVzbiMcWQ7s8lyZlnjrmx+9NaP2O/ZH3PfwtTCkX6qQgghhBBCiOOABMEJtvT9hqiTGTW09/LVp9agm2DXFC6qKuEzH5lIReHQTwA3GMPQeeE3P8fX28vld/wIu+vQ+gqPBs1mp3ByOYWTy+Fca6y7vS0cCu/Zuom1/32J9/+5DICUzKy+dhInVFA4qXzUn5fRG7DC3oZge4f6rr6+vgrY8t24JmdZgW9pOvaiVFTHobVrSJ1VMOQTwymqgi3XjS3XjXtGX3sNwxPA39Qdrhz2N/WrHlZD1cN9rSUcRamoGVI9LIQ4uhmmwQHvgb4gdwjBbk+gJ+6+FBSynFlkubLIdmZTllFGdUF1OOSNDHdDY26b+6Br9Om+qB7BAC7NxS2zbxm274MQQgghhBDi+CVBcIL9fPnmqDPaA+gmpDlt/Pu20yjMHN5A8+1nn2LXujrO/twt5I0tG9Z9j6TUrGwmnzifySfOB6wT3bXs3smeLZusE9Ft3cy2VW8DoCgqeWXjKQ6Gw0UnTCG7qHjYgkzDG8Df0B3V1zewv++Xdi3XhaMsA8fJaVbwW5KGeph9nIeb6rLhHJ+Jc3xmeMw0TAKtnqjWEr6dB+itbe67X6h6ONRaoijValsh1cNCiATxBDzRrReCwW6rpzWq9UIo7G33tmOYRtx9uW1uqzrXZQW4EzInRIW4kduyndlkODLQ1OHvvR7qA3zfe/fR1N1EYWoht8y+RfoDCyGEEEIIIYZFcqRTx7E97b1xx7u9gWEPgXevr+PNpx9n2sIzmH76omHd92hTNY2C8RMpGD+R6rPOA6C38wCNH2ymcetm9mzZxMbXXqb2Xy8C4EpLp2hyOUXlVjBcNLkcZ0rqQR/H9OtWa4XdVrWvr76LQHMPmNZ2LcuJoySNlLmFOErTcJSkoabYR+Q5P//yHdy3fQlNKhQacMvEj3P+6T884v0qqoI9z409zw0z88PjhicQVTnsa+ym+52mvj7FKtjyU8IBsSPYXkJNl+phIcSh0Q2dA74DhxTs9gbi//xUFdWqyA2GtxOzJvZV6PYLdnNcOWQ5s3DZkufTMedPPF+CXyGEEEIIIcSIkCA4wYqz3DTECYOLsw7+EdJD0dPRzvO/+QXZRcXU3HTzMRnUudMzmDjrRCbOOhGw2mC0NtQHq4atthI7at8D0wRFIbdkbFSv4ZwxJQT29Ub19fXv7YZQ7plmx1GaTkplHvbSdBwlaWjpjlF5bs+/fAd37liCR7P+3Bo1uHPHEoBhCYPjUV02nBMycU7oVz28vzc6IO5fPZxqC5+QLtx7uCBlwP7HQohjT4+/J+7J0tq98YPddm87Zugdtn5SbCnh8DbXlcvkrMmxwW6wHUOOK4d0RzqqIv/eCCGEEEIIIUR/EgQn2NfProjqEQzgtmt8/eyKYXsM0zB44f578HZ1cem378LhGt6QOVmpqkbe2DLyxpZRWXM2AN6ebho/2ELjlk20b9qNr66V1rpNmM52uh1j0BTro76mAxyl6aSfNtaq9C1NH7Y+uQEjgE/34dN9eHWvdd2IuB5x26/78epefr5jCR41+rE9qsJPdywhq2whTlcWbnsqTs2J0+bEbXPj1Jy4bC7s6vBVKCuqgj0/BXt+ClRGVA/3+PE39eBv7LIqqJu66XqrEQKhFF3Blu8OVw2H2kuMVpAuhDh8uqHT7m2PDXEHCXYje9xG0hQtqn/uCVknRPfU7RfsZruycWrOUX7GQgghhBBCCHFskiA4wS6eVQJYvYL3tPdSnOXm62dXhMeHwzv/9zQ7697nzM98kfxx44dtv0cL0zDxN/fgqe/AW9+Jo95gbONYxgZKIQMMu0lPSi8Ngd3sa99B094tdPvbYTPYcjOwleagFmdhFKcTyHHiNwNWSGv4Bw1vfYYvKvANzR+oR+Wg1PgBdJuq8LlXvzroXTXAhYZT0XCpNlyqA6dmx605cWouXDY3Lpsbpz0FlyMNlz0NlzPduq25cNlcOLXocNmluazAWXPjtAXHytykTsggLRiWm4ZJoKU3XDnsb+zGu6ODnjUR1cNp9ujK4UKpHhZiJJmmSW+gNxzeDiXYPeA9MGC1bpo9LVyJm5+ST3l2eVR1bmTom+XMIsORcUx+ImVY1T0FK+6CjnrILIWa70HlFYlelRBCCCGEEOIYIEFwErBnriF18n2kdzeRmlqIPfMWYHiC4PqN63j9yUeZcsppzPzo2cOyz0NhmiYBM9AXjvYLTaMqYwcITsP3GWg8MoANeEnvcVHSlc/YzgLGdxcxobeEFMPq/+hRvHzg2s3WjJ1sde1ii2snexzNmEow5BgHtoBCXoeD/HYn+W095G9sw11rVQr7NYO2rABtOTqdeSrd+RqkOnCoDpyaE7tmJ82RRo6ag0Nz4NCscYdmzYkZ0xw40HB278fe/iHO1h04WrbhaPsQh6HjNE0c6cVcn6qzzxZ7YqI83eBXZR+n19eJ19eFx9+DJ9CDN9CLJ+DBo3vw6D48hh+P0YPXDOBRFDyKgldR6FYU9isKXlWhNzjmURS86uEFsSoKTtWOS3NYYbHmxG1347Sl4CpOwTXWRYaRRkl3HmO6c8jvzCB7fxoZ211ohvWYhmriyzbx5ykY+TaUMU60whRcmX3BtMvmwqFKL2IhAkYgHNqGQ9xgD93ItgyR23yGL+6+bKqtr3euM4eKnIpwhW442HVlRY05NKnqH1Z1T8GyL4M/2DKqY7d1GyQMFkIIIYQQQhwxCYIT7Pntz3PnG3eGP0bb2N3InW/cCXBEJ4sxTZPOjlaeu+9npBcUUPnJy6nvrI8buB4sgI1sXxCec5Cq18iQ9rAqYPtRFTU2UFUdFOi5TOwupqy7iNKufIo7c3H7rY8R66pBa2YXDWM66MjdS3eeH3822O0OCrXplGmzOa9fOGtX7dEhrWp9eVs7aNm+jX0ffEDj1k3s274dY6vVziOzIL+v1/DkKeSPn4BmG6Adg2laVV4Nq2D3aqhfDY1rwN9jbXdlQckcKP+YdVk8G9Lyue3lO/jru7uYvftC0nzZdDnaeG/sMq47cRzVh9Ij2DStgMHXDb5O8HYFr3eBtzN83fB24vV04PUdwOPrwuPtxOvvpjfQjdcfDJkDvXh1Lx6McLjsUfvCZE9EqOxRrKC5XbPxoaLyjhrc5gKP20QZo1LiK2Cip4QJ3lLrclsJeZuzgR6gh1atgzWuBnY4G9juauBDZwP7Utqw2x0DViyHg+N+t4cyP3LcqTmPip6jz29/nvveu4+m7iYKUwu5ZfYtctKpo4hpmnT7u+OGtwMFu52+zgH3l+5IDwe7hSmFTMmZ0neitIhgN8dpXabZ0+TNlUOl+61/U/29EOjtux7v9qBzPNbPgZ2vg94vqPf3WhXCEgQLIYQQQgghjpAEwQl233v3xfRS9Oge/vet/2XD/g2HHMBGVsbWrMqnqMPF8yc38uvnLzys9WmK1heUqlbFa/+gNMOR0Td+sGA1VA07wLxQVW3/OTbVht7pw9fQhW93J/4G62RuRpffWqiqYB+TgmNyOvYSq6evfUwKZcPVYqAkm8KS8bCwBgC/z8u+7dto3LqJPVs3Ub9hLZtef8X6ntntjJkwmaLyKRSXjaUorZf0ri1W6NuwGrr3Bb+5TiiqhNnXWqFvyRzImQhxgpgT3F/g9A/XYupWVXC6L4fTP/wfTvjIzEN7HooCjhTri/wBp6mAO/h1UAGfFST7uoLBcv/r3VEhc9QcXzemtxOfrxOPfwce/3q8updeVcGbqtBkZmAY41GNMuz6eCq845nVfToqVtCuE6DD3kizs4G9rib2uBvZ7d5Dm72XAwp4AA8GXtPAg47HCGAM8BH3gwm9PuMFxYcUOIeux2uxEdyuqbHV3wczUm8qicPnN/zxQ9xBxvyGP+6+7Kq9L8R1ZTEtdZpVnRsxFgp0c1w5ZDozh7U/+FFFDxw8lA0Eg9dQADuUOVG3g/ONwOGt0eYCuxtsbusy9NU/BA7pqD/874cQQgghhBBCBEkQnGBN3U1xx7v8XSzesjgmgI0MSjMcGQOGrtq7DejNW0g9dxafOWlKTHgb7/ZAAWwiGD1+fLu68DW00FPfhb++E70j+AuyAraCFFzl2VbgW5qGoygVxX7o4dnhsjuclEyZRsmUaeGxzr172LPq3zSuX0Xjh9tY8/wGVptWqJtm81KcZVJUOo+iqkrGzDodW+kssEV/rNo0TfyeAJ4uP71dfno7fXi6/Lz21NZwCByeq2u88sRmOts8aDbV+rKr/a4rcbfZgtdVm4JmV1FV5fArAW0OsOVASs5h3V0BnMGvTLBCHH93RLVydOWy2buDQJuOf7+Cv8NBygE3ed2VVHTND+9TVTuwa7uxK9txmFuwKzuwKfWg6ATACprDVcsqXkWxxjQHHocbj92F1+bEY3PisdnxqDa8mh2PquJBxWMoePx+vH4/HjrYb+pW2Gz6rTYcug+P7kU39bjP+WDsqj0qcHZqEeFyv+A4tP2pzU/FfVPpp+/8NPxmjUN1YFftMdcjLx2q47CC6GQ1XFXSpmnS5e+KCm8jT44WL9jt9A9crZvhyAj30C1OK2ZG3ozYvrqhgNeVQ4ot5eiu1jX0oVfOHnZIG9w2QJh+UJozOpSNDGnTCoLhbQrYg5dRt+OEujFzgrdtLhio/c6vZljtIPrLLD285ySEEEIIIYQQESQITrDC1EIauxtjxotSi3jpspcOa58Nmzfy5KvfpHz+qVxw3TeTPjwwPAF8DV3hKl9ffRd6a1+gZctz4xifiaM0HUdpGvbiNFRngoMq04TW7VC/yqrybVhFetNaKnQfFQATCgicPId9jnJ2d2bQsLebhp0fsmVNM6xZifLUG6RkFuNKH4fdVQxqIX5vKp7uALp/6K00fL06by3dfsRPR1EIh8WqTcXWP0iOCZiD2+zagGGzZlOwBfcXGT5H7yt2/6qmobgywZUZf62APfgVSe/yhU9KZ30V07VvJujBCmBNwZ5rw54L9qwA6Zm95KR1oimdg1cue7siwujg5RADXr9qw+tMxeNIx+NMwWt3W0GzzYXX7qTX5rACZs2GR7XhUdVwKw2PAl5Mek0DLwYeM4DH8NPh6WCvvjfcA9ob8OLRPQNWkrZ527h5xc1DeyEEqYoaDoXtmh2bagtfjxy3q9Ehsl2149Acca8f0pw4j9P/MYfy79pgVdJnlp0ZdUK0wU6WFgp2A2b86k+H6iDblR0OcUvySsLX4wW7Wc6shL3JFsUwhhDKHkpIO0jl7ECVrgejOeKHrDY3pOQNEMqm9FXcxoS6AwS5NhckwxsgNd+L7hEM1rprvpe4NQkhhBBCCCGOGUnwm+jx7ZbZt0QFFQAuzcUts285rP31dh7g+ft+RkZ+AWd99ktJFwIbPh1/Y3dUe4dASy+hT+tr2U6ryndeoRX8lqShupPgZdrVDA2r0Xe9R+/ODfQ2fIjHo9JrZNCr5OFJqaHXeQMetcAa61bw1PrxdPkxQ50IlIU4M7sxAo0YgUY83U10t78DphXg2RxppOeNJ3vsJPLKJlM4qZz0nDRcaXb+71fv0960hoDnNTA6QU3H5jqVrKJqrv7BfPSAie430ANG32XUdTNmWyB43QhuD8S7nz90X52Az8DbExjgMaz9D4tQKG2LH0RHhsuhsFmzqaj2YIBtU9FcdrQTstEqcnH4Ath7/Ni6/FZ7kR1eFI8JuKyv1CLUvBS0Aje2Mak4JqXiKEzF5tBQbXEqpU0TAt5+LTDi91y2+7qx+7pIC4fMwe3dzdH3171D/+Y40sCRCs604PVscKZxlm8tqW3zOGnXBeE+0m+Pe472nHe5t/xa/KqGX1Hwqyp+RcWvKPjAGlOwrgN+THymQQAdvxHAb/jx6T78hj/mul/30xvoHXBOwLBOFDlQgHokbKpt8FBZtbOpdVPMidE8uofbV97Ot1Z+a4DvsEKmMzMc4o5NG0tlXmX0CdP6nTjNbXMP37+1htFX3TrkkDZeABtZJTtAe4Mhv+76Ue0DV8Km5BykcjYypB3CnGQIZ0dT5RVs2eLgzZcDdAWySLO1s+B0G+WVFyd6ZUIIIYQQQohjQBIkbMe30EeUh+ujy/984F6629u46oc/x5mSOtzLPbT1BAwr9G3oxLe7C39DJ/69PeHQV81w4ChJI6W6wKr0LUlDSxu9M9CbponPo+Pp8tHbabVi8HT56G3voXfvHjwtzfS2d9PbHcDjd9FrZOA35wPzY3fWAa5UO+40O+50B9lFfdddqXbc6XbcaQ5c6da4K82Oza5h6DrNuz6kcetmGrdspPGDzWxfvY7tq0FRVfLGjaf4hCmkpHXT0vMaEKxENToJ9PyLseUl2OwaNjtDbOg7ckzTxNDNAcJmM+p23CA5cmyQsDl02+8JRO8zYGJE3B6sFbBDgUxNIUNTyPQZZBzwk/5hB35FoRfQTZNOHTp0k05MuhWFbk3FtAUrpqOqolU0mxPN5kazFcZvz2FX0dyx1dA2mxU2a6qBDS+a6bG+jB40swct0I1mdKPpXWiBThR/7In98HbBgT3cuGcqTS2fwGZYJ0tM9+Vw+vZPUHigk6pdPz6MP1El+DF2Z8SXy/r4fOi6zRG8TAVbbsTt6PvomoOAasOv2fGpGn5Nw69q+BQNv6oSUBR8wXDaryj4FPAr4DdN/GYgKlgeKJSON8dn+Djv/VlM6rgQvyMHu6+VbZnLeGHW+3xp1pfiBruZjszY1himGd16IBTCtjWA/4PhDWkDnvh/HAej2gauhHVlQXro9kFC2oPNsblBk0OHkbLl7Sb++2oWgeAba12BHP77qgpjmyg/qTDBqxNCCCGEEEIc7eS3uSRw/sTzh+VkTqufX8r21e9wxvWfoXDSCcOwsqEzdQP/3h789V1W8Fvfhb+pO/yxfDXVhqM0Hde03HCLBy3DOaxrMHQDT3eA3i4fnshgt8tPb2ec691+jED8tFDDh1vVcdlN3Kl2MjNduHPTcY8pwpWZ2hfsplkhrzPFjqoeekWgqmmMmTCJMRMmUX3WeQD0HOig6YMtwRPRbWbja//F19sb594B1r/8GGnZAVIyMknJyiIlI4uUTOvLZh/dE0UpihKu4E000zQxjMgq6diK6P4V073eAGa7F6XNg9Lhw93pJb3LjxbRqsOvgceu0GtX6LEpVkAcMPB79LhV16Hb5uGdnw5ICX5ZJ/ZTtf4V0n23W/e1YevXMMNmOGlvvonlM7+JgoGCjmIaEdf1vkszEHHbHzFmfeHzo3qD1w0/qukHw48Svu5DMTyoRkdw3Bd8HANFMVEwrX1jBm9b2xyYODFIixiz5hooioGi2lBsdhTNhmKzoWj28G00O6rNjmJzgM2GanOA3Y6ipaDa7Pxq1QJyPZfgd1pvMPmduYzv/iTXvOnkKjYCAVTTi2n4giGsjx6/BwJezIDHCmgDPsyAF1CC7y0oUddNlOBrTom4Hdyu2MDmwrQ5QXNhalZIbtryrZBcdWGmWIG5qbmsNgiaC9MWvNQcmJrTCt/D1/tdqnbrumoD0+x7rZlgWv8Lj5mmGXcc08T0Ad7QWL/9mCbgwzR9wIGoxzFD1yMfA8JjoceKty3ycUyzb33Ra+03Nzxm9m2LfE7hMWt+7Ny+62b4gYkYi/O9ifw+RM6NfD4xc/seJLyGiOcKJqYR/X3BhL07D8T8XAr4DN78v20SBAshhBBCCCGOmATBx4jGrZtZ+fe/MPnEBcw658IRfSzTMAk091hhb6iv755uCFYwKS4NR2k66aeWYA+FvlnOQ/7otN+nWydN6/QFQ93Y6+GTqnX58PYEBqwCdbhtVoWu2yTd0UVBdjPulN24ej7ATQsutQO308BdXIarbAr2slkopadbJwgaZSkZmUycfSITZ58IgGHo/Oqqj8Wd6+vtZeXf/xJ3mzMllZTMTCsYDgfEmaRkZkeNp2Zl4XAf5Sei6kdRFDRNQdOOPJTWO30RfYe7SGnqxr+vF4zgi82mYi9MwV6Yir0oFUdRKvbCVNSUvlDW0ONXPUe15/CbBAZs7dGvKjpO2NyyO/4/5wHTQcteA9MwMU0F09QwDRXTtIVDPGublVgZRjCwMkwM0wQjIuw7ymQDRr/iXkNzkMpVPPJmQpZ0hPzBr+5EL2RkKVbcjqIEL0NjSsS26O3h+cF/xhQlODf0z9ogc6P2HW9+1NzIx+jbYdTjwqDzFaVvv4D1JmJw20BvTna1HmYbDyGEEEIIIYSIIEFwEuh+fx8Hln+I3u5Fy3KScfZ4UmcNPYD0dHXx3H0/JS0nj7M/d8thB3rx1pFSnY++3xNu7+Br6MTf0I3ps1oUKA4Ve3EaafOLrPYOpenYclwo/apjTcPE0xMnzA1W6VoVvL5gta51PeCL33NWURWrEjf4lVuSFqzQteNKc/Rdd3hxd23Ctf89tKbV1kndupqsnWgOKJ4JJXOgZBGUzIWciQOfyT2BVFUjPS+fzpbmmG3pefnccM8D9Bxop6ejg+6OdnpCXwfa6Wlvp+dAB6176qnfuI7erk7iJXqa3R4MiyOC46wsUjIySc3Mwp2ZRWqw0tidnoGqHT99O7V0B1q6A1d5dnjMDBj49/X0BcRN3Xg27qdn1d6++2U6sRelRn05c90xfzeGy1+//XrcsCgtx8XVP4jTzuQQhashTauSsX+I3Hc9NKff9pj79JsXMQfTxAhdN8AIzgsF1aF1GIaJ0d2Nd+cuvB/uwrdrF77d9Rg+PyYKm8uvIuDbFNNb2+aYwpwTOiEQsL50P2YgAP6Adan7Mf0BCPghEMAMRNz2W3PN8HVrDgTrgINlq8E64XD5aKiGOPT3L+q2qqDYbKh2G4rdHvyyodqCtx121NC4I7jNbkexO1AdNhSHw7rtsOaoDkff/ZyO4HYHitPapjocKE4HqsOO4nCi2LTokNK62i/U7AswrbGIQDOcrgbnRga4RISy9JvfL5Q9lt6MOlwD/z0e3k/QCCGEEEIIIY5PEgQnWPf7+2h/ditm8OPnervXuu0L4J5hfRw8/PnWfh+PtT5qavLfhx7EPBDggi9/E61Xxd/TE3U/MzjXGovYH33hjmdLK52v1EOwGklv99L21GbantkSHsOm4ihOJWVOAbaSNMxsF167hrfHT0eXn942H727G4Nhb0QrhmDoa4YqKPuxOVTcoQA33U5OUWq4l264/UKo326aHafbFhumBXywbz3Ur4Ld71mhb8sWwk80dzJMPM0KfEvmQOEM66PZR4mFV17LS3+4n4CvLyCwOZwsvPJa7C4Xma5CMgsO/rFhQ9fp7TxAd3sbPQc6+kLjDitI7uloo6ejneZdH9Lb0Y4eiHOSL0XBnZYebkERrjSOaEuRmtkXKtudruH8ViQFxabiKE7DUZwWHjNNE6PTj7+xC18wHPY3duPZ0grB9zQUu4ptTAqOojSrirgoDXtR6rCcEHHBxybx38c2Rb2BYnOoLPjYpCPeN0SGeQok6H0AU9fxfrCN3jVrwl+BHTuwA3ZVxVlRgfvkKlKqq3FXV7Plu08S8L0BBF/Hwd7a9kA387/2jWFdl+nzYXq9GF4fps9rXfd4+657vZiR2yJuh697vXFud1v7PBC8ny9im9eL4fNhBgIc0akaNQ3F6QwGxE7rutOB4nT1XXc4g9scqE7nALeD1we4be0zdDtiWxK+AZcoCz42ic1PbKbCpuBWodeAzQGTimH6eyyEEEIIIYQ4vkkQnGAHln8YDoFDTL9B+5JttC/ZNqR9zOQkZhafhPF0K3tpHb7FmdYJ7PcVpdFhQoc3QE9jL71bD+DrjRMQBjlTbVawm2YnM99N4cTMmDA38rrdcYipkmnC/m3QEAx8G1ZBYx3owZA0JQ9K58LMy6FkNhTPss5kfxSbuvAMAFY+8Qid+1tIz81j4ZXXhseHStU0UrOySc3KPuhc0zTx9nT3BcQHOoIVxn3BcXdHO/t2fEB3ezu+3p64+7E7XeHq4lBbitTMLNzBthSR467UtKM2FFIUBS3DgZaRg6ui7/Vm+iOrh63e2b3rW+h+t+/vkJYVUT0cbDFhO8Tq4VD/0Df/bxtdrV7Scpws+Niko7qvqN7eTm9tLT3B0NdTtxaj22qLoGVn466uJvPii3FXV+OeMR01NfoEmYbyHuEQOCxAwHyX3evrUFQVRdVQNRVV1YK3+66rodta/NuhMVXVUN1ucLsTkpGbgQCmz2cFyKFA2uM5rOA5NmgOBtodB4Jz+4XQHo/1g+JI2Gz9QmhnX2DsdKA6nNEB9XCH0kn0b06JQ8XtUlBM6+9+igbVNshxJM8ahRBCCCGEEEcvCYITTG8fuO9f5oUTw1V4Uc0Ngx+h7Wjey1tLniJv3DjmnHexFRopVk7q9+n4vDp+j47PG8DXq+Pz6Pg8fry91jZvrx9fr47XE+Akpxr3Y7mKbrBuT3e45UJBrju6/UIw8HWFTp6WakMdhr6sUbpbokPfhtXQ22Zts7mhuBrmfdqq9C2dC5ljIxo0HjumLjzjkIPfI6EoCq7UNFypaeQUlxx0fsDnC7eoCFUZd3e003ugne5gi4qOfU00bt1E74EDmGZseKRqGu6MUHuK2LYUofFQsKzZRveEeIdDsas4StJwlKQBY4Bg9fABX1TlsL+xC8+m1nARu2JXw6FwZEisugb+Z7v8pMKjNvi1qn0/oPf9vmpf34cfWhs1DWdFOZkfu8gKfauqUIqK6GlvpXN/C82t++la8U86W1vo2r8/eNmC19sV97H8/l6euuvbw/4cwmGxpoVDY0XVgmGxdd0KkvvGY4LnqPtaoXP4tjJwKN1/X+GQWosTcLs0lJQ0FDUjev/99hdvX7bIucHngGmiBAJWIBwIoPj9mH4dRQ+A3x/8CqCE2mn4fOALoPh9wev+QUNpo7cXs709fjW0xxO33c0hCbXTcLlig+eIgHqwUBpHcJvdhWJ3gN2BaneCzWGd3NBmD19H1VA0O6g2FFWzPrmjm2CYtD2zEcWM/hmqmArtSzccUssoIYQQQgghhIhHguAEM9w21DjVtYbbRvopVvim+42YXrqdLQd465lfous6juyLeOnlhnDvXW+3f8Dfi+0urS/AzUshJc1OTpoDo24fmlePmW/LdnH9t+YN63MelL/Xqu4NBb4Nq6HtQ2ubokL+VJhyQV/omz8VNHkZJwObw0FGXgEZeQcPKwxDx9PV1a81hRUWd0dUHbc17qGnoz2qJUYkV2paX1AcDohjT4yXmpmF3eVOmh6kiqKgZTpxZzpxT4msHtbx7+0J9x32N3bTs7YF852m8Bwt22m1lAi2lnAUpaIF+3Ifab/x0RRoa6O3tjYY+tbiqavDCLa1MXNzYMZ09NMX4h+Tjy/VTdeBA1bA+/oKOpc9Se+Bjph9OtwppOfmkZaTS97YMra89XrcSvWUzCwuuOUbGLqBaegYpoFpGBi6bl0aBqauW5eh20bf7dDcqNtGxH3778vQg4/Vt5/QeLzHs+4fwPBH7yveWuLfN3ot8d50SSaqYkPTbNhUO5pmR1U1bKoNVbOjudPRUrNQVWuOptpQ0VBVzbquaqioqErwEhUVpe//poISvB26rpjWVsUMfqGgmipK6D/TukfonlHXA9YXPSqKogW/rJA+vtAJ/ob83Yg7anikIlgIIYQQQghx5CRBS7ANvQGmmCa2iIAqYJrUtnp55btv0Nvlx++JDmhN08Tf/RyGv5X0MZ/kQIuJOx2rt27/9gtpjnC/XVeaHZs9/geXuydmRPUqBqsiMePs8SPyvAEwdGjZ2hf61q+CfRvACAbjGaVWa4e5n7KC36JqcKYNuktxdFBVzQpuMzJhbNmgc03TxO/1hNtSdHe009vRQXdHW1/18YF2WnbvpGd9HZ6uzrj7sdkdES0qIiuMo0+Ml5KZhSs9HVUd/Q/5K3YNR2k6jtL08JhpmugHfH2tJYInqPNs3N9XPexQUdPs1icMgn+F9XYvbc9sIdDuIWVaLmgqiqqgaIp1cjJNAU2xPhavKqCO3Mm6TF3Hu3UrPe+/z4H33qNt/To6m/fisdvwOOz483PxzZpCr02jx+vB29sD7Xvg/T3hfbjSM0jPySUtJ5fCSSeQlptLek4eabl51mVOLs6UlKjHHTe9kg1/+xcz0k8mxZZBT+AA6zrfYNo1ZzJ2euWIPNfRYpom6MGT6AUM61I3MUNjumFVmeqmFUIHdIyAYfUT9huYAQMjoGMGdEzdum3q1pcRMKz7B6x9mOF9GeHK1f6X1pdV2aqYgB7sRW8Ev4LXlfClAmaw5/SwfEMG32yYOgYGJgaGYoXjhmJgYGCYOiYGOro1zzQwTD+GoYfvZ5i6dZ841w3TwCA4FnVdj5pj0jcW3h45H4PTC68kxZYes/7eQOwbH0IIIYQQQghxqCQITrBt7X48doVpLi18YpgNHp0Gv8kJlZnBFgxWKwZXMNj9cM1/ePPprZx61fWcdPFlw7KOUNXgiFYTHtjTV+Vbvwr2rAFfMLRzZlih7ym3WKFvyRxIPzo/4j5SOpYtY9+v7iXQ2IitqIiCW79C5oUXJnpZI05RFBwuN45CN1mFRQedrwf89B44EAyMreC4/4nxulpb2bdjGz0HOjD02Ep4RVFxZ2TECY37qo4j21XYHI6ReOrBtSjYMp3Y+lUPGz6dwN6ecOVw19uNxJwxLGDSuXwnnct3Du3BNMVqMRN1qQ4wHrzUrCBZUUE3dHTdj6+3G297K77OA3h7OvH7PPgw8Kugk4cxdiFmqRV8KaZBistJVmoKjtRUXOmpONPScGWk485Ix5WZgTszE5vTERFiq33XQ+sIKOhdvr71aApjU6aRlq+h6FbYmGrPZF7eOWSnlGP49GBwavQFmsEQ1Qz0HzPCIas1bkTN79tmYAYixvrfr19gG9pX+HHjha399hN6nCM7O9whCP5ZK1roTQS17/sf+fpwRL7BYG2PeY1oEbcj9hu1n4jbfdf73S/e6yDicem33iN5kyOqynuAqu/YCnE99j79qrQH2lftn57jxPzzsKl9bW8Chp/a1lco59j/914IIYQQQggxsiQITrC0HCcNrV4a/IGY8bNunB4zf++Obbyz9BEmVM9h3kWXDOtaUmcVDF/w6zkAjWuswLdhtdXjtzNY3afaoXAGVH0iGPrOhdzJ1i/swqr0CwQwdd06CZTfz4EXX2TfT3+G6bVaJAT27KHxju8BHBdh8KHQbHbSgpWjB2OaJp7uyBYVsSfG6+5op+mDLXR3tOP39Mbdj8Pt7qsujtOWoq/qOAtnauqwVN6qDg3H2HQcY63qwa439gw4N+eqCkyDgSs69WBA2S8QDd02dYOA10eg10PA60Pv8RHw+TF8AQy/9WUaZugD+KiKiqI4cJFPSkohaqqGGvoIffC/uLqDX2E9eOnBS1P8+UPQ/7EUQ6F98VbaF2897H0OSbzq65gAVI0IORUUhxZ9W40IN/vfjgw74wWi/R83KnSNCGvjBbGRa0+SdiqJoqgq2ij+bHr1zw/ybsuLVGafFq5ir2t7hValftTWIIQQQgghhDh2SRCcYAs+Non/PLIeXe/7ZVvTTBZ8bFLMXG9PD8/d+xPcGZmc84XbkudM57rfaulQvyp4UrdV0LyZ8Gd1cybC+FOswLdkDhTOBLvriB4yHJZGfIVv6zqm3w/hIDUAeuRcHTPgD84PzgkE5/sDmHrEvoJzrf3rfY+lW/u1Hi8Qu6+IuaYeAH/E2gLWiZNCQW/U89B1CMT2jI77PfB42PONb7L3pz9DTUlBdbv7LlNTUMK3U6zLFOt21Hhq3/2s8VRUtwtFG/22CImgKArutHTcaenklow96Hy/12OFxQcig+PoE+O1NTbQsHkDvZ0H4p7EStVswaDYCo1TM7NwR7SliPxyp2eg2Yb2z7SW5Yx78kkty0lK1cBv8Bi6TldbK92tLXTu309Xawud7S10tu6na38LXW376WrdH1M5rSoKbhScPb04PT7c/gBuh5PMsWVkTZlKzuw55Mw7CVtabDuXvorZiBA6srrW6FcxGxNcR1TJxrlvaPuBQSqhM88dH6xu7V91Gi84jaxW7V/t2ledGnX7OA9QxeH5yA2fZfkD97GrfmN4TNNsnP35WxK4KiGEEEIIIcSxQoLgBBuz710qNj7DtnHn43Xm4PS2MmnX84zZdylEfAzUNE3+9dD9dOzbyxXf+7HVW/UgTNO0ztDer7o0HJAG+gWem5fD23/G7GrGdOdjzrwSxi4Ih6nhQLNzL2bLDszWXdC2C7Oj0ZpjKKC5MVPHYLo/iunKBVc2ZqMNc3cA9C2Y/g3R4WlEINo/QI0KdgPW2efD1+N8nH9EKQqKzQY2G0rwC5sWPBu8DUXTUOw20PptdzhQbSlWsGq3WfM1zdput6H0nx/abg8+VnD73h//eKA/ZNI/+lGM3l6Mnh7M3h6M7m4CLS0YPT0R4/ErWQd8ui5XX7Cc4kaJDJQHGo8Im1V3xLZQ2Ox2o9jtB3/wJGZ3usgscJFZMOagcw1dp7fzQEylcXfUCfI62L97Fz0H2tH98U8o5UrPCPcvHvjEeFkcKO3E2arEfKS8vaATfcM660Rrrfuty/2hyxa629tjTiZmcziDJ13Loah4LM7sfBwtrai7dmPf04jLr+NAwV1RgfvEebirq3HPqsZeUjKkAFRRg2EpACP3pkP3200DhuPppx08+BditE1deAYAK594hM79LaTn5rHwymvD40IIIYQQQghxJCQITrB9P/0RhS0dFO55O2q88Tur6fjHsnA4us3fw2bTwzRdw/eNb7Mtsro0XngaCFh9JA9LLmDAC38H/j6E+cETNKlqMPDsRbE1oGhNMeGpYtMgMjy12VCcDqsi1WaLDlPD4WlEYBq53W4DLSKMtWnBx7Kj2IJha2i7Pbi//tuj1hbct90WEdYGg9kEV8ju/8tfCOyJ/ei/rbiYort+cND7m4aB2dsbDoaN3l6M7h6M3p5wUGz09GD0hLYHx3t6owJlf3t7zLZ4Va8DUex26886JWWQKubUqLD5oFXMqakodnvSVWCqmkZqVjapWdkHnWuaJr7enugq4wPtdLeH+htbJ8Zr/nA7Ow+04+3ujrufcalTYz5Svmv5RljeN8eZkkpaTi7puXnkjS0jLSeP9OCJ19w2O7b6PegbNuKpraX31Zcwe3oA0PLycFdXkfLxy3BXV+OaPh3V7R6W79VIyTh7/OifBFOIIzR14RkS/AohhBBCCCFGhATBCRZoiX8mcNPnR2/aiaJpdNhUalUYoyhMd2moabbgx5PVYGCpWdV1Ns0aU1WrujQ8R7VCWlswqI2cG9q25hEU/wEUxURRAdVEUUBRTVBAUUHJm4hSXAklVSils1AKp6E43X1harK0qjgGFdz6FRrv+B6mxxMeU1wuCm79ypDur6gqSmoqamrqsK7LNE1MrzciRO7uC5UPGjYH5/f0EmhuxtgVHToPtUUGAJoWFSwrocrk/pXMg7TMCFcxR4TUiss1KgGzoig4U1JxpqSSXVRy0PkBv5/eA9FtKZY/cC+7ujeyq3tjzPzLvvO/pOXmkp6Ti8NtvXFjBgJ4t2yhZ80aepf/h941tbTt2mXdwWbDNWUKWZdcYlX7VlcNudo3mYzKSTCFEEIIIYQQQoijhATBCWZLCRDoif1jsKXoTJjzFj5D5T87ZuE2NC6Z8D4ptvgfHx+QQd/Z5WM/Id0nb5Bt1y2D4lngTD+0xxbDJnRCuH2/updAYyO2oiIKbv1Kwk8UpyiK1UbC5YKc4d236fNFBcOR1cpmVNA8cBWz3taOf8+eqHHT5zuUJ2gFwqnxW2NE91YeYDwiaA6FzIrbfURvnNjsdtJz80jP7fuL+8bix+hsaY6Zm56XT1llNYH9++l98y3a319D75o19K5bF24ZouXnkVJdTfYnrrCqfadNS/pq36Ea1pNgCiGEEEIIIYQQRzEJghOsYL6dxld0TL0vFFI0g4J5CuaNK/j348/S7l/L5Z+/jpTJE4MTQlV5SuztwbYNdvuvF0FXU+wCM8fChI8c/hMUwybzwgsTHvyOJsXhQHM40LKyhnW/ZiAQDI8HqGDu6VfFHBU2W/cxuroI7NsXNX7IfZiDfZNjK5lTB2iZMciJ/1JSmD1hCq/t24seETCrhkFFSycfnHkW/t27rUGbDdfUqWRddhnu6ircVdXYS4qPumpfIYQQQgghhBBCHBoJghMs83N3gver7HvfRaBHw5aiUzDLQ+bN97D2g1Y2rq7j5MuvZuxpl43sQs76ISz7Mvgjwiy7G2q+N7KPK8QoU2w2tPR0tPThrXAP92E+xCpms1+7DH/b4fVhTgVmZKWxuSgHj92Gyx+gorGVggM9uGpqyL7yStzVVVZvX5drWJ+7EEIIIYQQQojR1bFsWdJ9algkPwmCE63yCjJvgcwVd0FHPWSWQs2PaMk+if/8/DbGzajkpEuuGJV1ABC1ju/1jQshBpXoPsxN3/s+Je1dlLR39VuYQulvfj2saxJCCCGEEEIIkTgdy5ZFnUcosGcPjd+9A6Onh8yPfzwpT6gukkPSBMFvv/02999/P+vWrUPTNKqrq7ntttuYNm3akPfxxBNP8Nhjj7Fz507S09M5/fTTue2228jNzR3BlQ+DyiuiAle/x8Oyb9+Kw+3mvC99HVXVErIOIUTiDbUPc8uDvyewZ0/MuK2oaARXJ4QQQgghhBBiOJk+H4GWFuurudn62tccdduzYQMYRvT9vF6avn8nTd+/EyJ+j4y6dDpR3C5Up6vv0uVEdbmDl67Y+zmd1rlznANvP5Jz4IjRlRRB8Msvv8znP/950tPT+fjHPw7Ac889x1VXXcXf/vY3KisrD7qPX/ziFzz00EOMHz+e//mf/2Hv3r0sWbKEd999l8WLF5OZmTnST2PYrPjzg7Tuqeey7/yQ1KzsRC9HCHEUKLj1K1HvCAMoLhcFt34lcYsSQgghhBAxlr7fwM+Xb2ZPey/FWW6+fnYFF88qSfSyhBBDdLgtGfSubgLN+9AjA97wV9+Y3t4ee2dFQcvOxpafjy0/PyYEjpT/la9geHoxPV4Mrwez1xN96fHib+/A9ESOe63z3gyhLWE8itMZERA7Bwma3bHbI+fFCZxD1xWnC9XlRLElRZR5UMnauiPh3z2fz8f3v/99nE4nTzzxBBMnWidEu/rqq7n00ku58847efbZZwfdx5YtW/jTn/5EeXk5Tz31FO7g2e4XLlzIN7/5TX73u99x++23j/hzGQ7rX1nB+lf+zfxLr6RsZnWilyOEOEqEfqAk4w8aIYQQQghhWfp+A68t+R1P8gTFzhb29ORx75IrgZslDBbiKDBQSwZfYyPuqdOiq3hDXy1W0Gv29MTsT7Hb0fLzsOXnYx83Dvec2eGw15YXvCzIx5aTg2K3h++39aM18T8RWlxM3uc+e1jPzTRNTL/fCoh7PZjeiEuPxxr3eKzWhb3BoDk03i9ojpxv7G/FHxk4B8cJBA5rndjtUYGz6rZC4oEC59D2+PNiq6ZDFdCq0wmH2WIj7uvkDuscXIn+HT3hQfAbb7xBU1MTn/jEJ8IhMMCkSZO48MILeeqpp9iwYcOgLSKWLFmCYRh87nOfC4fAABdffDEPPPAAS5cu5etf/zq2JH3XYOPK/7LyiUfobGkGILu4lAWXXZXgVQkhjjaZF16Y8B8qQgghhBBiYGue/wN3KX8gRfEBUKq08EPzD/zvUp3tTf+DqmnYNA1N07CpCpqqYNcUNFUN37ZpCjZVta73v60Fx+LdDl+Pva2pivQTBd79x+8Z+97PKTCb2afks3v21znxosML1cSxad+v7o36FCZYLRlafvmrqDE1NTUc6Lqnzwhez+sLefPz0fLy0LKyDuvv3q6PX0f+gz/DruvhMb+msefj13HC4T01qy2hwwEOB1pGxmHuZehMvz8iGPZienqtS+9BgujwfE9MEK0fOIC5tzcqcDY9Hkyf7/AWqWlWO43IoDgYJKsuJ0rosl/g3Pq3R2NfJx4P+351b8J/Z094Mrpq1SoAFixYELNtwYIFPPXUU6xatWrQIHj16tWD7uPxxx/ngw8+YMqUKcO06uGzceV/eekP9xPwecNjnS372Pz6q0xdeEYCVyaEEEIIIYQQYjjd5HuUFDU6kHArPn7EA/D2A+Ex3VTQUTGCX9Z1a0yPGlPRzThjqPhR8Qav66gYpoqO0m+foe0KJhqGomIqKqaiBb9UQMVUI24rGijWmHVdQ1Gt+6BqKIoKqgaqDUVRUdTguKqhqDbruha6raGGLjVbeI6mqda4ZkNVbajBcFzRbGiqDdVmzdeC22y20Bw7qmbDHtxujVu3Nc2GTbP2bVMVVDU6fHv3H79nxurv4lZ8oEAhzWSu/i7vgoTBIsy/Zw/xYlsTGP/Yo8FK3jzUlJSYOYZhopsmumESMEw8hone40c3QmMGhgEBw7DGTJOAboavh+bphsl/Wrdyy4ltdNSlEOjRsKXo5FYe4MnWraib96EqCqoCmmK9yaMqoKrWpXVbCW7DmqsSvo8S3KaGtgfvF7od2qYqCkrE/dTweN/cwUJuxW5Hs9shLW34/oAGYOq6VckcbIEREzhHBcz9tkcFzsH7e73Wydxb2+JuH4g/ThX3aEt4ELxr1y4Axo0bF7MtNBaaM5CdO3eSmppKTk7smZQi9zGUIFgPvpvS1NR00LnD4bmH/0B3Z1f0oNfPcw//gfQJh/s+jhBCCCGEOBKhY0E9otIm2Y32cawQ4tCZPW3UE3sycBNQTv6iFVaYBqaho+s6pqljGjqmYWDoOpg6hqGDoWMYBpjWddOw7kPwvhg6immgmjqaaW1XTAOCc8APpoES3IdiBrebVtysmGbw0gjuxwCC17FuW9H04fUTHQ0m4A9e90aMGyZRIbuBgqGojDF72a8AUX8+OgWvfJv3Vv4iuE8lfBn5zK3bfWPWPCXmPqF1meEo0bqPqSgRY6Hx6LHQ/czgdmJu999/xHqUOGODrjHy+Sl9+1T6j8fbZ/RzjLpU+rb3f4799xH5fQ09Xv/nbKJghqcrUc+9/+PEjvV/zL77K/T/MwreV1GY59LRe2NPjKa5DZ596kF0EzBNjOCXCdaledjtd+O6VluOL8+D+6N9oaMPuPrAn3jkgfrhe6AjpCjBV2fwzz58m76gWAluUMAKooHQy0UN3gj9CUbuR428b8TjRD1GxEBV/XcAABcrSURBVL76HpPgYwX/jCPuq9I3NzSOTUFNByUjtA83Cu6+tRD5+NZlzsPPYfTGhuCq26S+fmT/fA52DJvwILirywpB0+K8AxAa6+zsHHQf3d3dcUPgQ9lHSHOz1Z7h6quvHtL8kfMhf6ypSfAahBBCCCGOb83NzZSVlSV6GUOSPMexQoiB5Q+8admTo7eMg1JIgrhglB1JVWJkrCiOZX8IxsQxehVY+uqorWMJ6UD6AFvfGLV1iMHEe50Ao5T1DXQMe7z9y35QM2bM4LHHHiM/Px9Ni32nVgghhBBCHPt0Xae5uZkZM2YkeilDJsexQgghhBDHt4MdwyY8CA5V7IYqgyOFxtLTB3qXw5Kamhr3/oeyjxCXy8XcuXOHNFcIIYQQQhy7jpZK4BA5jhVCCCGEEIMdw8Y2Nhllg/UBHqx/cKSysjK6u7tpbW097H0IIYQQQgghhBBCCCHEsSrhQXCoauHNN9+M2RYaO1hlw5w5cwbdR1ZWFpMnTz7SpQohhBBCCCGEEEIIIcRRKeFB8Mknn0xhYSH/+Mc/2L59e3h827ZtLFu2jGnTpjFt2jQA/H4/27ZtY8+ePVH7+PjHP46qqjz44IP09vadMXHp0qV8+OGHXHzxxdhsCe+CIYQQQgghhBBCCCGEEAmhmKaZ8FNb/ve//+Xmm28mPT2dCy64AIDnnnsOj8fDo48+SmVlJQD19fXU1NQwb948/va3v0Xt4+c//zl//OMfGT9+PDU1Nezdu5cXX3yR0tJSFi9eTGZm5qg/LyGEEEIIIYQQQgghhEgGSREEA7z11lv89re/Zd26dSiKwuzZs7n11luZPn16eM5gQbBpmjz55JM89thjfPjhh6Snp3P66afz1a9+ldzc3NF+OkIIIYQQQgghhBBCCJE0kiYIFkIIIYQQQgghhBBCCDEyEt4jWAghhBBCCCGEEEIIIcTIkiBYCCGEEEIIIYQQQgghjnG2RC/gaNPU1MSLL77Iyy+/zPbt22lrayM3N5eTTz6Zm2++mbFjx8bcZ/ny5fzxj39ky5YtuFwu5s+fz9e+9rW4cwGeeOIJHnvsMXbu3BnudXzbbbfF9DpetWoVy5cv55133qGhoQG/38+ECRO46KKLuOaaa7Db7SPyPRAHl0yvk5A9e/bw4IMPsnLlSpqbm8nMzGTKlCl8+tOfZv78+cP6/MXQjOTrZP/+/Tz11FOsXbuWtWvXsm/fPqqqqnjqqacGXE8gEOBPf/oTzz77LHv27CE3N5dzzjmHL33pS6Smpg778xdDk2yvk0iNjY2cf/75dHd38+lPf5qvfe1rw/KcxaFLttfJ7t27eeCBB1i1ahVNTU3k5OQwbdo0Pv3pTzNr1qxhf/5iaJLp+ESOY5NXMr1OQuQ4Nrkk288cOYZNTsn2Ookkx7DJI9leJ8fDMaz0CD5Ev/jFL3jooYeYNGkSc+fOJT09nXXr1vHWW2+RkZHBY489Rnl5eXj+448/zp133smYMWM499xz6ezs5Pnnn8ftdrN48eKYF2po/+PHj6empoa9e/fy4osvUlpayuLFi8nMzAzPPeWUU+jo6GDevHlUVFTg9Xp5+eWXaWho4JRTTuGPf/wjqipF34mQTK8TgDVr1nDjjTdiGAYf/ehHKSkpob29nXXr1nHmmWfy+c9/flS+LyLaSL5O3n77ba699lo0TWPSpEls2bLloD/0br31Vl544QWmT5/OggUL2L59O//5z3+YNWsWjzzyCA6HY0S/HyK+ZHudRPr0pz/NqlWr6OnpkYPoBEum18kHH3zAFVdcgdfrpaamhnHjxtHU1MRLL72E3+/nN7/5DYsWLRrx74mIlUzHJ3Icm7yS6XUCchybjJLpZw7IMWyySrbXSSQ5hk0eyfQ6OW6OYU1xSF566SVz9erVMeMPP/ywWV5ebn7qU58Kj+3fv9+srq42TznlFLOlpSU8/tZbb5kVFRXmF77whah9bN682ZwyZYp5wQUXmD09PeHxJUuWmOXl5eaPf/zjqPkPPfRQ1H5N0zR7e3vNyy+/3CwvLzefe+65I3qu4vAl0+ukvb3dPOWUU8yzzz7bbGpqilmT3+8/7OcpjsxIvk6am5vNd999N/waKS8vNy+//PIB1/Lyyy+HHzMQCITHf/Ob35jl5eXmX//618N+nuLIJNPrJNKSJUvMqVOnmn/84x/N8vJy8+c///nhPD0xTJLpdfLNb37TLC8vN1944YWo8TVr1hzSa0wMv2Q6PpHj2OSVTK8TOY5NTsn0M0eOYZNXMr1OIskxbHJJptfJ8XIMK2+zH6IzzzyT2bNnx4xfe+21uN1u3nvvvfDYiy++SE9PD9dee23Ux5xOOukkTj75ZP7zn//Q2toaHl+yZAmGYfC5z30Ot9sdHr/44osZP348S5cuJRAIhMdvuummmI9PuVwurrvuOgBWr1595E9YHJZkep08+uijNDc3c9dddzFmzJiYNdls0iEmUUbydZKXl8fcuXOjXiODeeaZZwD40pe+hKZp4fGbbrqJtLQ0nn766UN+fmJ4JNPrJKSlpYW7776ba665hhkzZhzGsxLDLZleJw0NDQB85CMfiRqvqqoiKyuLtra2Q3puYvgk0/GJHMcmr2R6nchxbHJKpp85cgybvJLpdRIix7DJJ5leJ8fLMawEwcNI07SoHz6rVq0CiNu3asGCBei6zvvvvx8eCx3wLliwIO789vZ2Pvjgg4OuI3RAFLkWkTxG+3WyfPlysrOzmTdvHnV1dfz5z3/m4Ycf5t133x225ySG35G+Tg7V6tWrSUlJobKyMmrc5XIxe/ZsNm/eTFdX12HvX4yM0X6dhNx1112kpqZyyy23HPG+xMgb7dfJxIkTAXj11Vejxmtra2lvb2fevHmHvW8xcuQ4VgyFHMeKg5FjWDEUcgwrhkKOYUeGvIU6TFasWEFXVxdnnnlmeGznzp0AjBs3LmZ+aGzXrl1R81NTU8nJyRl0/pQpUwZdy9KlS4H4fzlEYo3268Tn8/HBBx8wbdo07rjjjpheOPPmzeP++++P6cUmEms4XieHoru7m5aWFsrLy+P2Ywztf+fOnUyfPv2wHkMMv9F+nYS89NJLLF++nD/84Q+kpKQc0b7EyEvE6+Szn/0sr7zyCl/72td48cUXKSsrC/dXW7hwIV//+tcPe99iZMhxrBgKOY4VByPHsGIo5BhWDIUcw44cCYKHQUtLCz/84Q9xOBx8+ctfDo93d3cDkJaWFnOf0FhnZ2fU/HgHRQPNj+cf//gH//nPf5gzZw4f/ehHD+2JiBGViNdJR0cHuq6zYcMGtm/fzs9//nPOOOMM9u/fzz333MNLL73EHXfcwa9//evheZLiiA3X6+RQhKok4u17OPYvhl8iXidg/Zty1113ccEFF3Daaacd9n7E6EjU66S4uJgnn3ySL33pSyxfvjw8XlJSwiWXXEJWVtZh71sMPzmOFUMhx7HiYOQYVgyFHMOKoZBj2JElrSGOUE9PDzfffDN79+7le9/7XtTZDEfbqlWruOOOOygoKOCee+5BUZSErUVES9TrxDAMAHRd5ytf+QoXXXQR6enpjB8/nnvuuYeSkhJeeukl9uzZMyrrEYNLpn9PRPJK5OvkJz/5CX6/n29/+9uj9pji8CTydbJt2zauuuoqVFVl8eLFrFmzhuXLlzNnzhxuvfVW/vCHP4zaWsTgkunnjhzHJi85jhUHk0z/lojkJcewYijkGHbkSRB8BDweD5/73Oeora3lG9/4BpdffnnU9tTUVIC4PYlCY+np6VHzB+pfFG9+pLq6Oj772c+SkpLCX/7yF4qKig79CYkRkcjXSeT9zjjjjKi5DoeDU045BdM02bBhw6E+LTHMhvt1cihC754e7r8/YvQk8nXy1ltv8eyzz/KNb3wj5gRPIrkk8nUC8O1vf5vW1lZ++9vfUllZidvtZvz48fz0pz+loqKC3/72t9KvMQnIcawYCjmOFQcjx7BiKOQYVgyFHMOODgmCD5PP5+MLX/gCb7/9Nl/60pe48cYbY+aUlZUB8XuUhMYie5uUlZXR3d0ddZbDweaHbNiwgZtuuglN03j44YeZNGnS4T0pMewS/TpJSUmhoKAAiP8PYujgyePxHNLzEsNrJF4nhyI1NZW8vDzq6+vD1Tfx9h9ag0iMRL9ONm3aBFgHSBUVFeGva6+9FoCHHnqIiooKfvSjHx3W/sXwSPTrpKurizVr1jBx4sSYX7ZUVWXOnDl4PB527NhxWPsXwyPRxyeR5Dg2eSX6dSLHsckv0T9z5Bj26JDo14kcwx4dEv06OZ6OYSUIPgx+v59bbrmF1157jZtuuokvfvGLcefNnTsXsN6B6u/NN99E0zRmzZoVHpszZ054W7z5WVlZTJ48OWp869atfOpTnyIQCPDHP/7xoCfgEKMnWV4noTNbbtu2LWb+9u3bAaTyJoFG6nVyqObMmUNPTw91dXVR4x6Ph/fee4+KiooB+6+JkZcMr5Py8nIuu+yymK9Qn7WKigouu+yyI3odiiOTDK8Tn88HQHt7e9ztbW1tANjt9sPavzhyyXJ8AnIcm8yS5XUix7HJKxl+5oAcwya7ZHidyDFs8kuG18lxdQxrikMSCATMW265xSwvLzd/+MMfDjp3//795v+3d38hVd9/HMdfJ8o/pQtzTdhO0Nb8HreSzBvzGC6t0f5CFmHp3FiwmyILJhyDoAYjC2EXuXCEg9qWOk5L2Cz7nwy2dkw2GROPC5d0Ydm2XFGCdtrndzF2+J10zebx+PXb83F1/Hw/5+vnI288L956PicrK8vk5eWZ3377LTweCASMx+Mxmzdvjpjf3d1tMjIyzGuvvWYGBwfD401NTcayLLN79+6I+ZcvXzZ5eXlm8eLF5uLFi1HYHaLFTnUSCASMZVnm7bffNkNDQ+Hx9vZ2k5GRYfLz883w8PB4tov/aCLr5H6WZZl169b94/XW1lZjWZbZuHGjCYVC4fGamhpjWZY5dOjQGHeFaLNTnYzmu+++M5Zlmerq6od6HqLLTnXy4osvGsuyTFNTU8R4V1e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      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"notebook\", font_scale=1.7)\n",
    "\n",
    "nations =['AT', 'FR', 'DE', 'IT', 'NL', 'ES']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(20,15), ncols=2, nrows=3, sharex=True, sharey=True)\n",
    "\n",
    "for iii, nation in enumerate(nations):\n",
    "    ax=axs.flatten()[iii]\n",
    "    \n",
    "    ax.set_title(nation)\n",
    "\n",
    "    df_nation_in_time = df_scores_in_time[df_scores_in_time.nation==nation].copy()\n",
    "\n",
    "    df_nation_in_time_counts = df_nation_in_time.groupby(\"party\").score.count()\n",
    "    df_nation_in_time_counts = df_nation_in_time_counts[df_nation_in_time_counts>=3]\n",
    "    df_nation_in_time = df_nation_in_time[df_nation_in_time.party.isin(df_nation_in_time_counts.keys())]\n",
    "    df_nation_in_time = df_nation_in_time.loc[df_nation_in_time.year.sort_values().index]\n",
    "\n",
    "\n",
    "    for party in df_nation_in_time.party.unique():\n",
    "        x = df_nation_in_time[df_nation_in_time.party==party].year.values.astype(int)\n",
    "        y = df_nation_in_time[df_nation_in_time.party==party].score.values\n",
    "        ax.plot(x,y,'-o', label=party_labels[nation][party])\n",
    "        \n",
    "        \n",
    "    ttt = ax.set_xticks(range(2002,2020,4))\n",
    "    ttt = ax.set_ylim(0,1)\n",
    " \n",
    "    ax.legend(ncol=3, prop={'size': 15})\n",
    "    ax.set_ylabel(\"(Pop) Score\")\n",
    "    ax.set_xlabel(\"year\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./SI_figures/figure_H.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
